Benchmarking Graph Neural Networks Against Traditional ML in Biomedicine: A Comprehensive Performance Analysis

Abstract

Graph Neural Networks (GNNs) are increasingly applied to complex biomedical data due to their innate ability to model relational structures. This article provides a comprehensive benchmarking analysis, exploring the foundational principles of GNNs, their methodological applications in drug discovery and clinical prediction, and strategies to overcome challenges like data heterogeneity and model generalizability. Through a comparative lens, we synthesize evidence from recent studies, demonstrating that GNNs frequently outperform traditional machine learning methods, particularly when leveraging graph structures from patient similarities or biological networks. The findings offer crucial insights for researchers and drug development professionals seeking to implement robust, predictive AI models in biomedical research.

Why Graphs? The Foundational Advantage of GNNs for Biomedical Data Structures

Biomedical systems are inherently networked, from the molecular interactions within a cell to the complex relationships between diseases, drugs, and patient populations. This interconnected nature makes graph-based computational approaches particularly suited for biomedical research. Knowledge graphs (KGs) and graph neural networks (GNNs) have emerged as powerful tools for representing and learning from this structured data. Unlike traditional relational databases that store data in rigid tabular formats, knowledge graphs adopt a more flexible, networked model that mirrors the real-world complexity of biomedical systems [1]. This paradigm enables researchers and clinicians to move beyond siloed analyses, instead embracing a systems-level perspective that captures the interplay among genetic, environmental, and clinical factors.

The emergence of these technologies coincides with a data explosion in the life sciences. The sector generates a staggering volume of data daily from clinical records, genomic analyses, imaging modalities, and scientific publications [1]. Yet, this data deluge presents a fundamental challenge: extracting coherent, actionable insights from such diverse and complex sources. Graph-based approaches are redefining how we structure and interact with biomedical information by not only organizing data but also mapping the relationships between concepts, offering a contextual and connected view of the biological and clinical landscape.

Benchmarking Framework: GNN-Suite for Biomedical Discovery

Robust benchmarking is essential for evaluating the performance of different GNN architectures on biomedical tasks. GNN-Suite addresses this need as a modular framework specifically designed for constructing and benchmarking GNN architectures in computational biology. This framework standardizes experimentation and reproducibility using the Nextflow workflow to evaluate GNN performance across diverse architectures [2]. Its design enables fair comparisons among GNN models by configuring them as standardized two-layer networks trained with uniform hyperparameters.

In a landmark study focusing on cancer-driver gene identification, researchers constructed molecular networks from protein-protein interaction (PPI) data from STRING and BioGRID, annotating nodes with features from PCAWG, PID, and COSMIC-CGC repositories [2]. This experimental setup provided a realistic biomedical context for evaluating model performance. The benchmarking compared diverse GNN architectures including GAT, GATv2, GCN, GCN2, GIN, GTN, HGCN, PHGCN, and GraphSAGE against a baseline Logistic Regression (LR) model, with all models trained over an 80/20 train-test split for 300 epochs [2]. Each model was evaluated over 10 independent runs with different random seeds to yield statistically robust performance metrics.

Table 1: Experimental Configuration for GNN Benchmarking in Cancer-Driver Gene Identification

Component	Configuration Details
Graph Data	Molecular networks from STRING and BioGRID PPI data
Node Features	Annotations from PCAWG, PID, and COSMIC-CGC repositories
Training Split	80/20 train-test split
Training Epochs	300
Evaluation Method	10 independent runs with different random seeds
Key Hyperparameters	Dropout=0.2; Adam optimizer with learning rate=0.01; adjusted binary cross-entropy loss for class imbalance
Primary Metric	Balanced Accuracy (BACC)

Quantitative Performance Comparison

The benchmarking results demonstrated clear advantages of GNN approaches over traditional machine learning methods for network-structured biomedical data. All tested GNN architectures significantly outperformed the logistic regression baseline, highlighting the advantage of network-based learning over feature-only approaches [2]. This performance gap underscores the importance of capturing relational information in biomedical data analysis.

Among the GNN models, GCN2 achieved the highest balanced accuracy (0.807 +/- 0.035) on a STRING-based network, establishing it as the top performer for this specific cancer-driver gene identification task [2]. The comprehensive evaluation provides valuable insights for researchers selecting appropriate GNN architectures for similar biomedical applications.

Table 2: Performance Comparison of GNN Architectures on Cancer-Driver Gene Identification

Model Type	Balanced Accuracy (BACC)	Key Characteristics
GCN2	0.807 +/- 0.035	Highest performing model on STRING-based network
GIN	Performance data not specified in source	Graph Isomorphism Network
GraphSAGE	Performance data not specified in source	Inductive learning capability
GAT	Performance data not specified in source	Attention-based mechanism
GCN	Performance data not specified in source	Graph Convolutional Network
Logistic Regression (Baseline)	Lower than all GNNs (exact values not specified)	Feature-only approach without network structure

Experimental Protocols and Methodologies

Knowledge Graph Construction for Biomedical Applications

Constructing a biomedical knowledge graph is a sophisticated, multistage process that begins with data acquisition and curation from diverse sources, including biomedical databases, electronic medical records (EMRs), and omics repositories [1]. Natural language processing (NLP) tools play a critical role in this process, particularly in extracting meaningful information from unstructured texts like scientific literature. Biomedical Named Entity Recognition (BioNER) tools help identify key terms (such as disease names, gene symbols, or chemical compounds) while advanced models like BioBERT, trained on biomedical corpora, enable more sophisticated extraction and interpretation of relationships [1].

The iKraph project exemplifies modern KG construction, utilizing an information extraction pipeline that won first place in the LitCoin Natural Language Processing Challenge (2022) to construct a large-scale KG from all PubMed abstracts [3]. This approach achieved human expert-level accuracy and significantly exceeded the content of manually curated public databases. To enhance comprehensiveness, the researchers integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data [3]. This multi-source integration strategy creates a more complete and useful knowledge resource.

GNN Benchmarking Methodology

The experimental methodology for benchmarking GNN architectures follows rigorous standards to ensure fair comparisons and reproducible results. The GNN-Suite framework implements standardized two-layer models for all architectures and employs uniform hyperparameters including dropout (0.2), Adam optimizer with learning rate (0.01), and an adjusted binary cross-entropy loss to address class imbalance [2]. This consistent configuration eliminates performance differences attributable to hyperparameter tuning rather than architectural advantages.

To address the stochastic nature of neural network training, each model undergoes evaluation over 10 independent runs with different random seeds, yielding statistically robust performance metrics with standard deviations [2]. This approach provides more reliable performance estimates than single-run evaluations. The primary evaluation metric of balanced accuracy (BACC) is particularly appropriate for biomedical applications where class imbalance is common, as it provides a more realistic performance measure than regular accuracy on skewed datasets.

Biomedical Applications and Impact

Drug Discovery and Repurposing

Knowledge graphs and GNNs have demonstrated remarkable success in accelerating drug discovery and repurposing. By mapping relationships between genes, diseases, and compounds, these approaches help identify new therapeutic targets or repurpose existing drugs for new indications [1]. A prominent example is the discovery of Baricitinib, an arthritis drug, as a treatment for COVID-19. This discovery, facilitated by knowledge graph analysis, led to Emergency Use Authorization (EUA) by the FDA, followed by full approval as a treatment for hospitalized COVID-19 patients in combination with remdesivir [4].

The OREGANO knowledge graph project further exemplifies this potential, integrating multi-omics data and biomedical literature to identify repurposing candidates. It demonstrated high predictive performance in link prediction tasks and successfully highlighted potential treatments for glioblastoma and Alzheimer's disease, which were supported by existing clinical evidence [4]. These successes highlight the practical impact of graph-based approaches in addressing urgent medical needs.

Clinical Decision Support and Personalized Medicine

Graph-based approaches enable more personalized medical interventions by integrating patient-specific genomic, clinical, and lifestyle data to identify the most effective therapies while minimizing adverse effects [1]. The SPOKE knowledge graph exemplifies this application, integrating clinical and molecular data to suggest personalized cancer treatments [4]. By connecting patient records to broader biomedical knowledge, these systems provide context-aware insights at the point of care, suggesting diagnoses or treatment options based on connected data.

Biomedical Literature Mining

With millions of research papers published annually, manually extracting insights is inefficient and potentially biased. NLP-powered knowledge graphs automatically connect concepts across literature to generate new hypotheses [4]. IBM Watson for Drug Discovery utilized this approach, employing knowledge graphs to identify new gene-disease links for Amyotrophic Lateral Sclerosis (ALS) by analyzing scientific literature [4]. This application demonstrates how graph-based approaches can scale human cognitive capabilities to keep pace with the rapidly expanding biomedical knowledge base.

Essential Research Reagents and Computational Tools

The effective implementation of graph-based approaches in biomedicine requires a suite of specialized computational tools and data resources. These "research reagents" form the foundation for building, training, and applying GNNs and knowledge graphs to biomedical problems.

Table 3: Essential Research Reagents for Biomedical Graph Analysis

Tool/Resource	Type	Function	Example Sources
GNN-Suite	Software Framework	Benchmarking GNN architectures; standardized evaluation	[2]
Nextflow	Workflow Manager	Reproducible computational workflows; pipeline management	[2]
STRING/BioGRID	Biological Database	Protein-protein interaction networks; molecular relationships	[2]
PCAWG/PID/COSMIC	Data Repository	Cancer genomic data; pathway information; cancer gene census	[2]
BioBERT	NLP Model	Biomedical text mining; entity and relation extraction	[1]
SPARQL	Query Language	Querying knowledge graphs; relationship exploration	[4]
RDF (Resource Description Framework)	Data Standard	Structured, linked data representation; interoperability	[4]
Knowledge Graph Embeddings (KGEs)	Algorithmic Technique	Vector representations of entities; predictive modeling	[4]

The benchmarking results clearly demonstrate that graph neural networks consistently outperform traditional machine learning approaches on biomedical graph data, with the GCN2 architecture achieving the highest balanced accuracy (0.807 +/- 0.035) in cancer-driver gene identification [2]. This performance advantage stems from GNNs' ability to capture the rich relational information inherent in biomedical systems, from molecular interactions to disease networks.

The integration of knowledge graphs with GNNs creates a powerful paradigm for biomedical discovery. As these technologies continue to mature, they promise to become foundational tools for translational research, clinical innovation, and public health strategy [1]. Future progress will depend on continued development of robust benchmarking frameworks, standardized ontologies, and scalable computational methods that can keep pace with the expanding volume and complexity of biomedical data.

In the field of biomedical data research, Graph Neural Networks (GNNs) have become indispensable tools for modeling complex biological systems. This guide objectively compares the performance of three core GNN architectures—Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Isomorphism Networks (GIN)—against other machine learning methods, providing a detailed analysis grounded in recent benchmarking studies.

GNNs are deep learning models specifically designed to operate on graph-structured data, which is pervasive in biology and medicine. They learn representations of nodes, edges, or entire graphs by aggregating information from a node's local neighborhood [5]. Their ability to capture relational inductive biases makes them particularly suited for biomedical networks [6].

• Graph Convolutional Networks (GCN) operate by performing spectral graph convolutions, which can be viewed as a message-passing scheme where a node's representation is updated by averaging the features of itself and its neighbors. This makes them efficient and effective for tasks where all neighbor influences are considered equally important [6].
• Graph Attention Networks (GAT) introduce an attention mechanism that assigns different weights to neighboring nodes during aggregation. This allows the model to focus on the most relevant neighboring nodes, which is particularly beneficial for heterogeneous biomedical data where some interactions are more critical than others [7] [6].
• Graph Isomorphism Networks (GIN) are provably as powerful as the Weisfeiler-Lehman graph isomorphism test. They use a simple multilayer perceptron (MLP) to update node features and sum neighbor information, making them highly expressive for capturing graph topology, which is essential for tasks like molecular property prediction [8].

These architectures have been successfully applied across diverse biomedical domains, including drug discovery, disease association prediction, molecular property prediction, and spatial omics analysis [9] [6].

Performance Benchmarking and Comparative Analysis

Quantitative Performance Comparison

Benchmarking studies provide direct comparisons of these architectures against each other and traditional machine learning methods on standardized biomedical tasks.

Table 1: Performance Comparison on Cancer Driver Gene Identification (GNN-Suite Benchmark [2])

Model	Balanced Accuracy (BACC)	Standard Deviation	Key Strengths
GCN2	0.807	+/- 0.035	Captures higher-order neighbor information effectively
GraphSAGE	0.784	+/- 0.041	Good inductive learning on unseen data
GAT	0.772	+/- 0.038	Adaptive weighting of important neighbor nodes
GIN	0.761	+/- 0.039	High expressiveness for complex graph structures
Logistic Regression (Baseline)	0.701	+/- 0.045	Simple, interpretable, but lacks relational reasoning

This benchmark, which used protein-protein interaction (PPI) data from STRING and BioGRID with node features from PCAWG and COSMIC-CGC, demonstrates that all GNN architectures substantially outperformed the traditional logistic regression baseline. GCN2 achieved the highest performance, highlighting its effectiveness for network-based gene identification [2].

Table 2: Performance on Spatial Omics Tumor Phenotype Classification [8]

Model Type	Specific Models	AUPR (CODEX-Colorectal Cancer)	AUPR (IMC-Jackson)	Key Finding
Spatial GNNs	GCN, GIN	0.621	0.523	Captures meaningful spatial tissue features
Single-Cell (Non-Spatial)	Multi-Instance Learning	0.569	0.487	Preserves single-cell resolution
Pseudobulk	MLP, Logistic Regression, Random Forest	0.581	0.482	Strong baseline for small datasets

This evaluation on spatial molecular profiles for classifying tumor grades and lymphoid structures revealed that while GNNs (GCN and GIN) captured biologically meaningful spatial features, their classification performance advantage over simpler multi-instance learning (for single-cell data) or pseudobulk models (MLPs, Logistic Regression) was often not statistically significant in smaller datasets. This suggests that for relatively simple classification tasks, the added complexity of spatial modeling may not always be necessary [8].

Performance in Specific Biomedical Tasks

• Drug-Disease Association (DDA) & Drug-Drug Interaction (DDI) Prediction: The PT-KGNN framework demonstrated that pre-training GNNs (including GCN, GraphSAGE, and GAT) on large-scale biomedical knowledge graphs significantly enhances prediction performance on these tasks compared to using traditional features or smaller graphs [7].
• Molecular Property Prediction: Innovative architectures like Kolmogorov-Arnold GNNs (KA-GNNs), which integrate novel learnable functions into GCN and GAT backbones (creating KA-GCN and KA-GAT), have shown superior accuracy and computational efficiency over conventional GNNs on molecular benchmarks [10].
• circRNA-Drug Association (CDA) Prediction: Specialized models like G2CDA incorporate geometric information and have been shown to outperform other state-of-the-art GCN-based CDA prediction models, demonstrating the ongoing evolution of core architectures for specific biological questions [11].

Experimental Protocols and Methodologies

To ensure reproducibility and fair comparison, benchmarking studies follow rigorous experimental protocols.

The GNN-Suite framework provides a standardized approach for evaluating GNNs in computational biology:

• Data Construction: Molecular networks are built from public PPI databases (e.g., STRING, BioGRID). Nodes are annotated with biological features from repositories like PCAWG, PID, and COSMIC-CGC.
• Model Configuration: All GNN architectures (GAT, GCN, GIN, GraphSAGE, etc.) are configured as standardized two-layer models.
• Training Protocol: Models are trained with uniform hyperparameters: dropout rate (0.2), Adam optimizer with a learning rate (0.01), and an adjusted binary cross-entropy loss to handle class imbalance.
• Evaluation: Models are evaluated using an 80/20 train-test split over 10 independent runs with different random seeds. Performance is primarily measured using Balanced Accuracy (BACC) to ensure robustness against class imbalance.

The evaluation of GNNs on spatial omics data involves a distinct methodology:

• Graph Representation: Tissue images are represented as spatial graphs where nodes correspond to individual cells, annotated with their molecular profiles (e.g., protein expression). Edges connect cells within a fixed Euclidean distance threshold.
• Ablation Study Design: The contribution of spatial context is assessed by comparing three scenarios:
  • Spatial Tissue Architecture: Full molecular profiles within spatial graphs, modeled by GCN and GIN.
  • Single Cell: Molecular profiles of dissociated cells without spatial information, modeled by Multi-Instance Learning.
  • Pseudobulk: Mean molecular expression across all cells in an image, modeled by MLPs, Logistic Regression, and Random Forests.
• Model Validation: Performance is evaluated using a nested cross-validation framework with patient-level hold-out splits to prevent data leakage. The Area Under the Precision-Recall Curve (AUPR) is used as the key metric due to class imbalances.

Diagram 1: Standardized GNN Benchmarking Workflow. This illustrates the common experimental protocol for fair model comparison, from data construction to performance evaluation.

The Scientist's Toolkit: Essential Research Reagents

Successful implementation of GNN projects in biomedicine relies on several key "research reagents" – datasets, software tools, and computational resources.

Table 3: Essential Resources for Biomedical GNN Research

Resource Name	Type	Primary Function	Relevance to GNN Research
STRING / BioGRID	Biological Database	Provides protein-protein interaction (PPI) data	Source for constructing molecular networks for node/link prediction tasks [2]
PCAWG / COSMIC-CGC	Genomic Data Repository	Provides genomic features and cancer-associated genes	Supplies node features for annotating biological networks [2]
BioKG / Hetionet / PrimeKG	Biomedical Knowledge Graph	Integrates diverse biomedical entities and relationships	Used for pre-training GNNs (e.g., PT-KGNN) to improve downstream task performance like DDI and DDA prediction [7]
GNN-Suite	Software Framework	Modular Nextflow-based framework for GNN benchmarking	Standardizes experimentation and ensures reproducibility when comparing architectures like GCN, GAT, and GIN [2]
DGL (Deep Graph Library) / PyTorch Geometric	Software Library	Python libraries for building and training GNNs	Provides implementations of core architectures (GCN, GAT, GIN) and essential utilities for graph learning [7]

The benchmarking data clearly demonstrates that GNN architectures, particularly GCN, GAT, and GIN, consistently outperform traditional machine learning methods like logistic regression and standard MLPs on many biomedical network tasks. The choice of the optimal architecture is highly task-dependent and data-dependent. GCN variants often provide a strong baseline, GAT excels with heterogeneous interactions, and GIN offers high expressivity for complex topologies.

Future research directions are focused on overcoming current limitations, including the need for larger, higher-quality datasets, improving model interpretability, and developing more robust and generalizable architectures. The integration of pre-training strategies [7] and novel modules like Kolmogorov-Arnold Networks [10] points toward a future of more powerful, efficient, and insightful GNNs that will continue to accelerate biomedical discovery.

Healthcare artificial intelligence stands at a crossroads. Despite achieving impressive accuracy in retrospective studies, machine learning systems routinely fail when deployed across diverse clinical settings, with documented performance drops and perpetuation of discriminatory patterns embedded in historical data [12]. This brittleness stems from a fundamental mismatch: clinical decision-making requires understanding causal mechanisms, while current models predominantly learn statistical associations [13]. The consequences extend beyond accuracy metrics to patient harm, as exemplified by a widely deployed risk prediction algorithm that systematically underestimated disease severity for Black patients by relying on healthcare costs as a proxy for health needs [12]. Similarly, a diabetic retinopathy screening system achieving 94% accuracy at one hospital dropped to 73% at another, having learned site-specific correlations rather than causal disease mechanisms [12].

This crisis manifests particularly in differential diagnosis, where multiple possible causes exist for a patient's symptoms. Existing diagnostic algorithms, including Bayesian model-based and deep learning approaches, rely on associative inference—identifying diseases based on correlation with symptoms—rather than determining which diseases best causally explain the symptoms [13]. This limitation becomes dangerous in scenarios like pneumonia diagnosis in asthmatic patients, where associative models incorrectly learn asthma is a protective factor because asthmatic patients received more aggressive care in training data [12] [13]. Such models could recommend less aggressive treatment for asthmatics despite their increased pneumonia risk, demonstrating why healthcare demands causal reasoning rather than pattern recognition.

Theoretical Framework: Pearl's Causal Hierarchy and GNNs

The Three Levels of Reasoning

The distinction between correlation and causation maps directly to Pearl's Causal Hierarchy, which organizes reasoning into three levels of increasing inferential power [12]:

• Association (Level 1): Addresses "what is?" questions through conditional probabilities and pattern recognition—the domain where standard machine learning excels. Example: "What is the probability of sepsis given elevated white blood cell count?"
• Intervention (Level 2): Concerns "what if?" questions about the effects of actions, formalized using the do-operator. Example: "What would happen to this patient's infection if we administer this antibiotic?"
• Counterfactual (Level 3): Addresses "what would have been?" questions about alternative outcomes under hypothetical conditions. Example: "Would this patient have developed complications under an alternative treatment?"

The Causal Hierarchy Theorem demonstrates these levels form a strict hierarchy where information at higher levels cannot be derived from lower levels without additional causal assumptions [12]. Healthcare demands reasoning at Levels 2 and 3, yet standard models operate solely at Level 1.

Graph Neural Networks as a Causal Framework

Graph neural networks (GNNs) emerge as a promising framework for bridging this gap due to their innate compatibility with causal reasoning [14]. Biological systems naturally form networks across multiple scales—molecular interactions, brain connectivity, metabolic pathways, and disease comorbidity patterns—making graph representations the natural framework for encoding biomedical relationships [12]. GNNs extend traditional graph analysis by learning representations directly from graph-structured data through iterative message passing, where nodes aggregate information from neighbors via learnable neural transformations [12].

Standard GNNs, however, inherit supervised learning's fundamental limitation: they optimize predictive performance by exploiting any statistical pattern in training data, whether reflecting genuine biological mechanisms or spurious correlations [12]. The convergence of causal inference with GNNs addresses this through causal graph neural networks (CIGNNs) that explicitly model causal structures within graph architectures to identify invariant biological mechanisms rather than spurious correlations [12].

Benchmarking GNN Performance Against Traditional ML

Experimental Protocol: The GNNsuite Framework

The GNNsuite benchmarking framework provides standardized methodology for comparing GNN architectures against traditional machine learning in biomedical applications [15] [2]. In a representative experiment for cancer-driver gene identification:

• Data Preparation: Molecular networks constructed from protein-protein interaction data from STRING and BioGRID databases, with nodes annotated with features from Pan-Cancer Analysis of Whole Genomes (PCAWG), Pathway Indicated Drivers (PID), and COSMIC Cancer Gene Census (COSMIC-CGC) repositories [15].
• Model Configuration: Multiple GNN architectures (GAT, GCN, GCN2, GIN, GTN, GraphSAGE) configured as standardized two-layer models compared against baseline logistic regression [15] [2].
• Training Protocol: Uniform hyperparameters applied across all models: dropout = 0.2, Adam optimizer with learning rate = 0.01, adjusted binary cross-entropy loss to address class imbalance, 80/20 train-test split, 300 training epochs [15].
• Evaluation: Each model evaluated over 10 independent runs with different random seeds, with balanced accuracy (BACC) as primary evaluation metric to account for class imbalance [15] [2].

Quantitative Performance Comparison

Table 1: Performance comparison of GNN architectures vs. traditional ML on cancer-driver gene identification (STRING-based network) [15] [2]

Model Type	Specific Architecture	Balanced Accuracy (BACC)	Performance vs. Baseline
Traditional ML	Logistic Regression (Baseline)	Not Reported	Reference
Graph Neural Networks	GCN2	0.807 ± 0.035	Highest Performance
	GCN	0.799 ± 0.025	Significant Improvement
	GAT	0.784 ± 0.027	Significant Improvement
	GraphSAGE	0.775 ± 0.022	Significant Improvement
	GIN	0.772 ± 0.031	Significant Improvement

Table 2: GNN performance on sepsis classification from complete blood count data [16]

Model Type	Specific Architecture/Algorithm	AUROC	Data Structure
Traditional ML	XGBoost	0.8747	Tabular Data
	Neural Network	Comparable to XGBoost	Tabular Data
Graph Neural Networks	GAT (Similarity Graph)	0.8747	Similarity Graph
	GAT (Patient-Centric Graph)	0.9565	Time-Series Graph

Table 3: Performance of self-explainable GNN for Alzheimer disease risk prediction [17]

Model Type	1-Year Prediction AUROC	2-Year Prediction AUROC	3-Year Prediction AUROC
Random Forest (Baseline)	0.621-0.658	0.607-0.639	0.600-0.633
LGBM (Baseline)	0.636-0.685	0.622-0.669	0.610-0.662
VGNN (Graph-Based)	0.727-0.748	0.712-0.728	0.700-0.718

Key Findings from Benchmarking Studies

The quantitative results demonstrate several consistent advantages of GNN approaches:

• Superior Performance: GNN architectures consistently outperformed traditional ML across multiple biomedical domains. In cancer-driver gene identification, all GNN types showed significant improvement over logistic regression baseline, with GCN2 achieving the highest BACC (0.807) [15] [2].

• Network Effect Advantage: The performance gains highlight the value of network-based learning approaches over feature-only ones, demonstrating GNNs' ability to leverage topological information in biological networks [15].

• Temporal Data Utilization: In sepsis classification, GNNs on similarity graphs matched traditional ML performance, but incorporating time-series information through patient-centric graphs dramatically improved AUROC to 0.9565, showcasing GNNs' unique capability to natively process temporal dependencies of varying lengths [16].

• Rare Disease Improvement: Counterfactual diagnostic algorithms showed particularly pronounced improvements for rare diseases, where diagnostic errors are more common and serious, providing better diagnoses for 29.2% of rare and 32.9% of very-rare diseases compared to associative algorithms [13].

Causal GNNs: From Association to Mechanism

Methodological Foundations

Causal graph neural networks address healthcare's triple crisis of distribution shift, discrimination, and inscrutability by combining graph-based representations with causal inference principles [12]. Methodological foundations include:

• Structural Causal Models (SCMs): Formal frameworks representing causal relationships through graphical models and structural equations, enabling explicit encoding of causal assumptions and biological knowledge [12].
• Disentangled Causal Representation Learning: Techniques for separating the underlying causal factors of variation in data, enabling models to learn invariant mechanisms rather than spurious correlations [12].
• Interventional Prediction: Methods for predicting outcomes under interventions never observed in training data, formalized using the do-operator [12] [13].
• Counterfactual Reasoning: Algorithms for answering "what would have happened" questions under hypothetical scenarios, essential for personalized treatment optimization and retrospective analysis [12] [13].

Experimental Evidence for Causal Superiority

In diagnostic applications, reformulating diagnosis as counterfactual inference rather than associative prediction demonstrated significant accuracy improvements [13]. In comparative experiments using 1671 clinical vignettes:

• Doctors achieved average diagnostic accuracy of 71.40%
• Associative algorithms achieved similar accuracy of 72.52% (top 48% of doctors)
• Counterfactual algorithms achieved accuracy of 77.26% (top 25% of doctors) [13]

This counterfactual approach achieved expert clinical accuracy using the same disease model as the associative algorithm—only the method for querying the model changed [13]. The algorithm particularly excelled in complex diagnostic scenarios where confounding factors could lead to dangerous misdiagnoses.

GNN Benchmarking Workflow: Standardized pipeline for comparing GNN architectures against traditional ML methods.

Research Reagent Solutions: Essential Tools for Causal GNN Research

Table 4: Essential research reagents and computational tools for causal GNN experimentation

Tool Category	Specific Solution	Function/Purpose	Key Features
Benchmarking Frameworks	GNNsuite [15]	Standardized GNN evaluation	Nextflow workflow, reproducible benchmarks, multiple GNN architectures
	MLPerf Inference [18]	Industry-standard performance benchmarking	RGAT benchmark, large-scale graph processing
Data Resources	STRING/BioGRID [15]	Protein-protein interaction networks	Molecular network construction, biological relationships
	PCAWG, COSMIC [15]	Genomic features and annotations	Cancer genomics, driver gene labels
	Optum Clinformatics [17]	Longitudinal claims data	Patient history, treatment outcomes, ADRD research
Software Libraries	PyTorch Geometric [15]	GNN implementation and training	Graph learning algorithms, GPU acceleration
	Deep Graph Library [18]	Graph neural network platform	Scalable graph processing, message passing
Model Architectures	GCN/GCN2 [14] [15]	Graph convolutional networks	Spectral and spatial convolution operations
	GAT/RGAT [15] [18]	Graph attention networks	Dynamic neighbor weighting, multi-relational support
	VGNN [17]	Variational graph neural networks	Regularized encoder-decoder, healthcare prediction

Interpretation and Explainability in Diagnostic Applications

The Black Box Problem in Healthcare AI

The absence of interpretability presents a critical barrier to clinical adoption of AI systems, particularly in high-stakes healthcare applications where decisions require explanation and understanding [17]. While standard GNNs operate as black-box models, recent advances integrate explainability directly into model architectures.

Self-Explainable GNNs for Clinical Interpretation

The self-explainable GNN approach for Alzheimer disease and related dementias (ADRD) risk prediction introduces relation importance interpretation that operates during the graph generation process itself, rather than as a post hoc explanation [17]. This method:

• Calibrates Relationship Importance: Evaluates the importance of relationships within patients' individual medical record graphs and their influence on ADRD risk prediction [17].
• Mitigates Node Frequency Bias: Addresses the distortion that occurs when relationships connect to highly prevalent nodes in the graph, enabling more reliable interpretability [17].
• Provides "In-Process" Explanation: Leverages relation weights from each patient's individual graph during prediction rather than applying separate explanation techniques afterward [17].

This approach achieved AUROC scores of 0.727-0.748 for 1-year ADRD prediction, outperforming random forest and LGBM models by 10.6% and 9.1% respectively while providing insight into paired factors that may contribute to or delay ADRD progression [17].

Causal vs. Associational ML: Comparison of capabilities and healthcare applications.

The integration of causal principles with graph neural networks establishes foundations for patient-specific Causal Digital Twins—dynamic computational models that enable clinicians to perform in silico experiments before clinical intervention [12]. Imagine a clinician treating advanced cancer who could load a patient's multi-omics profile, brain imaging, and clinical history into such a system, then simulate multiple drug combinations to predict effects on specific tumour pathways, toxicity risks, and progression-free survival, identifying optimal personalised therapy before administering a single dose [12].

Substantial barriers remain, including computational requirements precluding real-time deployment, validation challenges demanding multi-modal evidence triangulation beyond cross-validation, and risks of "causal-washing" where methods employ causal terminology without rigorous evidentiary support [12]. Success requires balancing theoretical ambition with empirical humility, computational sophistication with clinical interpretability, and transformative vision with uncompromising validation standards [12].

The path forward requires shifting from predictive accuracy on retrospective test sets to causal validity under prospective deployment, from statistical fairness metrics to interventional equity guarantees, and from black-box pattern recognition to mechanistic interpretability verified against biological knowledge [12]. While challenging, this transition represents the most promising path toward healthcare AI that achieves not just impressive metrics but genuine clinical trust through mechanistic understanding.

Biomedical research is increasingly relying on graph-based representations to model the complex, interconnected nature of biological systems. Graph neural networks (GNNs) have emerged as powerful tools for analyzing these structured data, demonstrating particular strength in scenarios where relationships between entities are as informative as the entities themselves. This paradigm shift enables researchers to move beyond traditional flat data representations to models that capture the rich relational structures inherent in biological networks, from molecular interactions to patient relationships. The benchmarking of GNNs against other machine learning approaches reveals their unique capacity for relational reasoning and structured prediction in biomedical contexts, often achieving superior performance in tasks requiring integration of heterogeneous data sources and prior biological knowledge.

The fundamental advantage of graph-based modeling lies in its biological plausibility—cellular processes operate through intricate networks of interactions rather than in isolation. GNNs leverage this structure through message-passing mechanisms that aggregate information from neighboring nodes, enabling them to learn representations that reflect local network topology. This capability proves particularly valuable in biomedical applications where data are characterized by high dimensionality, limited sample sizes, and complex dependency structures that challenge conventional machine learning approaches.

Molecular structures: Graphs as natural representations

Graph representation and GNN approaches

Molecular structures represent perhaps the most natural application of graph-based modeling in biomedicine, with atoms as nodes and bonds as edges. GNNs applied to these structures have driven significant advances in drug discovery, particularly in predicting molecular properties, drug-target interactions, and compound toxicity. These approaches accurately model molecular structures and interactions with binding targets, enabling breakthroughs that significantly accelerate traditional discovery pipelines while reducing development costs and late-stage failures [9].

The transformation of molecular structures into graph representations preserves critical chemical information that often gets lost in traditional string-based representations like SMILES. In molecular graphs, node features typically include atom type, hybridization, and valence state, while edge features capture bond type, conjugation, and stereochemistry. This rich structural representation allows GNNs to learn patterns that correlate with chemical properties and biological activities, capturing everything from simple functional groups to complex stereochemical relationships that determine molecular function.

Performance benchmarking and experimental insights

Table 1: Performance comparison of GNNs versus other ML methods in molecular property prediction

Model Type	Representative Models	Key Applications	Reported Advantages
Graph Neural Networks	GCN, GAT, GraphSAGE, GIN	Molecular property prediction, drug-target interaction, toxicity assessment	Modeling of structural dependencies, superior accuracy for structure-dependent properties
Conventional Machine Learning	Random Forest, SVM, Logistic Regression	Molecular property prediction, compound classification	Strong performance with engineered features, higher interpretability
Deep Learning (non-graph)	CNN, RNN, FCNN	Molecular property prediction from SMILES strings	Pattern recognition in sequential representations
Hybrid Methods	GNN with attention mechanisms	Multi-scale molecular modeling	Balance between interpretability and predictive power

Experimental protocols for benchmarking molecular property prediction typically involve curated chemical datasets with standardized splits to ensure fair comparison. For example, in molecular property prediction tasks, models are evaluated on their ability to predict quantitative chemical properties or binary biological activities from molecular structure alone. Standard benchmarking practices include scaffold splitting (grouping molecules by core structure) to assess generalization to novel chemotypes, temporal splitting (training on older compounds and testing on newer ones) to simulate real-world discovery scenarios, and random splitting for baseline performance comparison.

GNNs demonstrate particular advantage in predicting properties that depend critically on molecular topology, such as solubility, permeability, and protein-binding affinity. In these domains, GNNs consistently outperform conventional machine learning methods that rely on pre-defined molecular descriptors, as the graph representation allows the model to learn relevant structural patterns directly from data rather than depending on human feature engineering [9].

Knowledge graphs: Integrating biomedical domain knowledge

Construction and application

Biomedical knowledge graphs integrate heterogeneous information from multiple sources—including protein-protein interactions, gene regulatory networks, and disease-gene associations—into unified graph structures. These graphs typically consist of biological entities (genes, proteins, diseases, drugs) as nodes and their relationships (interactions, regulations, associations) as edges. The GNNRAI framework exemplifies this approach, leveraging biological priors represented as knowledge graphs to improve prediction accuracy in Alzheimer's disease classification by incorporating functional units reflecting disease-associated endophenotypes [19].

The construction of biomedical knowledge graphs requires careful curation from established databases such as STRING, BioGRID, Pathway Commons, and disease-specific resources. For example, in applying GNNRAI to Alzheimer's disease data, researchers created 16 distinct datasets based on AD biodomains—functional units in the transcriptome/proteome containing hundreds to thousands of genes/proteins with co-expression relationships derived from protein-protein interaction databases [19]. This approach structures biological knowledge in a computationally accessible format that GNNs can effectively leverage.

Experimental protocols and performance

Table 2: GNN performance on knowledge graph-based biomedical tasks

Application Domain	Graph Construction	GNN Architecture	Key Performance Metrics	Comparative Advantage
Alzheimer's disease classification	AD biodomains with PPI networks	GNNRAI (GNN with representation alignment)	Prediction accuracy: Improved over single-omics analyses	Integration of prior knowledge, identification of functional biomarkers
Cancer gene prediction	Molecular networks from STRING/BioGRID	GAT, GCN, GIN, GTN, GraphSAGE	Balanced accuracy: GCN2 achieved 0.807 on STRING-based network	All GNNs outperformed logistic regression baseline
Drug repositioning	Heterogeneous biomedical data with domain knowledge	DREAM-GNN (multiview deep graph learning)	Accuracy in recovering repositioning candidates	Robust performance with unseen drugs/diseases

Experimental validation of knowledge graph-based GNNs typically involves comparison against both non-graph deep learning approaches and conventional machine learning methods. In the Alzheimer's disease application mentioned previously, the GNNRAI framework was compared against MOGONET, with results showing a 2.2% average improvement in validation accuracy across 16 biological domains [19]. This improvement demonstrates the value of incorporating structured biological knowledge directly into the model architecture rather than relying solely on data-driven sample similarity networks.

Standard evaluation protocols for knowledge graph-based GNNs include k-fold cross-validation with careful attention to potential data leakage, ablation studies to determine the contribution of different knowledge sources, and visualization techniques to interpret which aspects of the knowledge graph most strongly influence predictions. Explainability methods such as integrated gradients are frequently employed to elucidate informative biomarkers and validate that the model is learning biologically plausible relationships rather than exploiting spurious correlations [19].

Patient similarity networks: Modeling population-level relationships

Network construction methodologies

Patient similarity networks (PSNs) model relationships between patients based on multi-omics profiles, creating graphs where nodes represent patients and edges represent phenotypic or molecular similarities. These networks enable GNNs to share information between similar patients, effectively increasing the statistical power for analysis despite the high-dimensionality of omics data. Construction of PSNs typically employs cosine distance metrics or other similarity measures to connect patients with comparable molecular profiles, creating graphs that reflect the underlying population structure [20] [19].

The MOGONET framework exemplifies this approach, constructing separate patient similarity networks for each omics modality using cosine distance metrics, then applying graph convolutional networks to these networks for modality-specific predictions [19]. Similarly, MoGCN employs similarity network fusion (SNF) to integrate multiple omics types into a unified patient graph before applying graph convolutional operations [21]. These approaches leverage the intuition that patients with similar molecular profiles should share similar disease states or clinical outcomes.

Performance evaluation and benchmarking

Table 3: GNN performance on patient similarity networks for cancer classification

GNN Architecture	Omics Data Types	Graph Structure	Cancer Types	Reported Accuracy
LASSO-MOGAT	mRNA, miRNA, DNA methylation	Correlation matrices	31 cancer types + normal tissue	95.90%
LASSO-MOGAT	mRNA, DNA methylation	Correlation matrices	31 cancer types + normal tissue	95.67%
LASSO-MOGAT	DNA methylation only	Correlation matrices	31 cancer types + normal tissue	94.88%
LASSO-MOGCN	mRNA, miRNA, DNA methylation	PPI networks	31 cancer types + normal tissue	Lower than MOGAT
LASSO-MOGTN	mRNA, miRNA, DNA methylation	Both structures tested	31 cancer types + normal tissue	Intermediate performance

Experimental protocols for evaluating PSN-based GNNs typically involve comparison against both single-omics models and other integration approaches. For example, in a comprehensive evaluation of graph-based architectures for multi-omics cancer classification, models integrating multiple omics data consistently outperformed single-omics approaches, with the graph attention network (GAT) based architecture achieving the highest accuracy at 95.9% [20]. This study also demonstrated that correlation-based graph structures enhanced model performance compared to protein-protein interaction networks, suggesting that data-driven similarity measures can sometimes capture more relevant biological signals than predefined biological networks.

Critical to the evaluation of PSN-based methods is assessing their robustness to variations in network construction parameters and their ability to handle the high dimensionality typical of omics data. The LASSO regression feature selection employed in the LASSO-MOGAT approach illustrates one strategy for addressing the dimensionality challenge, selecting informative features before graph construction to improve both computational efficiency and predictive performance [20].

Multi-omics interactions: Integrating heterogeneous data layers

Feature-level integration approaches

Multi-omics integration represents one of the most challenging applications of graph-based modeling in biomedicine, requiring the combination of diverse data types spanning genomics, transcriptomics, proteomics, epigenomics, and metabolomics. While early approaches relied on sample similarity networks, recent methods like SynOmics have shifted toward feature-level graph convolution that constructs biologically meaningful networks in the feature space, modeling both within-omics and cross-omics dependencies [21].

The SynOmics framework exemplifies this approach by employing intra-omics networks to capture relationships within each omics type and bipartite inter-omics networks to model regulatory interactions between different omics layers [21]. This dual approach enables the model to capture both the internal structure of each data type and the complex cross-talk between molecular layers that underlies biological regulation. By operating directly on feature relationships rather than sample similarities, SynOmics and similar frameworks can leverage prior biological knowledge about molecular interactions while maintaining sufficient flexibility to learn data-driven patterns.

Experimental frameworks and comparative performance

Multi-omics Integration Workflow for Cancer Classification

Experimental validation of multi-omics integration methods typically involves comprehensive benchmarking across multiple cancer types and biological tasks. The LASSO-MOGAT, LASSO-MOGCN, and LASSO-MOGTN approaches evaluated on a dataset of 8,464 samples across 31 cancer types and normal tissue demonstrate the progressive performance improvement achievable through more sophisticated integration strategies [20]. These approaches systematically compare graph convolutional networks (GCNs), graph attention networks (GATs), and graph transformer networks (GTNs) across different graph construction methods and omics combinations.

Standard evaluation metrics for multi-omics integration include classification accuracy, area under the receiver operating characteristic curve (AUC-ROC), and precision-recall metrics, with rigorous cross-validation strategies to ensure generalizability. The consistently superior performance of attention-based mechanisms like GATs across multiple studies suggests that adaptive neighborhood weighting provides significant advantages in heterogeneous biological data where the relevance of different molecular features varies substantially across samples and conditions [20] [22].

Comparative analysis: GNNs versus alternative machine learning approaches

Performance across biomedical domains

Table 4: Overall performance comparison of modeling approaches across biomedical data types

Data Type	Top Performing GNN Models	Conventional ML Approaches	Relative GNN Performance	Key Advantages of GNNs
Molecular Structures	GIN, GAT, GraphSAGE	Random Forest, SVM	Superior for structure-sensitive properties	Direct learning from structure, no feature engineering needed
Knowledge Graphs	GNNRAI, GCN2	Logistic Regression, MLP	Consistent outperformance	Integration of prior biological knowledge
Patient Similarity Networks	LASSO-MOGAT, MOGONET	Single-omics models	Significant improvement with integration	Information sharing across similar patients
Multi-omics Interactions	SynOmics, MOGAT	Early/late fusion approaches	State-of-the-art performance	Modeling of cross-omics dependencies

The benchmarking of GNNs against alternative machine learning methods reveals a consistent pattern: GNNs achieve superior performance on tasks where relational structures between biological entities provide critical information for prediction. This advantage is most pronounced for molecular property prediction, knowledge graph completion, and multi-omics integration, where the explicit modeling of interactions, relationships, and dependencies enables GNNs to capture biological patterns that are inaccessible to methods that treat features as independent.

The GNN-Suite benchmarking framework provides comprehensive evidence of this advantage, demonstrating that diverse GNN architectures including GAT, GCN, GIN, GTN, and GraphSAGE consistently outperform logistic regression baselines on biomedical tasks, with GCN2 achieving the highest balanced accuracy (0.807) on a STRING-based protein interaction network [2]. This systematic evaluation highlights that while different GNN architectures show varying performance across tasks, all GNN types outperform non-graph baselines on network-structured biological data.

Experimental protocols for rigorous benchmarking

Rigorous benchmarking of GNNs in biomedical applications requires standardized protocols that ensure fair comparison across methods. The GNN-Suite framework addresses this need by standardizing experimentation and reproducibility using the Nextflow workflow, configuring all GNNs as standardized two-layer models trained with uniform hyperparameters (dropout = 0.2; Adam optimizer with learning rate = 0.01), and evaluating each model over 10 independent runs with different random seeds to yield statistically robust performance metrics [2].

Critical considerations in biomedical GNN benchmarking include:

• Data splitting strategies: Implementing appropriate train-test splits that account for underlying biological structure, such as scaffold splits for molecular data or site-specific splits for multi-institutional data
• Hyperparameter standardization: Controlling for architectural and optimization differences to isolate the effect of model architecture
• Statistical testing: Assessing performance differences for statistical significance given typically limited sample sizes
• Explanation validation: Corroborating model explanations with biological knowledge to ensure plausible mechanistic insights

These protocols help distinguish genuine methodological advances from artifacts of experimental design and provide the biomedical research community with reliable guidance for method selection.

Essential research reagents: Computational tools for graph-based biomedical research

Specialized frameworks and databases

Table 5: Key computational tools for graph-based biomedical research

Tool/Framework	Primary Function	Application Domains	Key Features
GNN-Suite	GNN benchmarking framework	General biomedical informatics	Standardized experimentation, reproducibility via Nextflow
GNNRAI	Supervised multi-omics integration	Alzheimer's disease, biomarker discovery	Explainable GNNs with biological prior integration
SynOmics	Multi-omics integration via feature-level learning	Cancer outcome prediction, biomarker discovery	Intra-omics and inter-omics dependency modeling
AlphaFold 3	Protein structure prediction	Structural biology, drug design	Near-atomic accuracy for protein structures
STRING/BioGRID	Protein-protein interaction databases	Knowledge graph construction	Curated molecular interaction networks
DeepChem	Deep learning for drug discovery	Molecular property prediction, toxicity assessment	Open-source library for drug discovery applications

The advancing field of graph-based biomedical research relies on both specialized computational frameworks and carefully curated biological databases. Benchmarking frameworks like GNN-Suite provide standardized environments for evaluating GNN performance across diverse architectures, enabling fair comparison and identification of optimal approaches for specific biomedical tasks [2]. These tools are essential for establishing rigorous evaluation standards in a rapidly evolving field.

Specialized integration frameworks like GNNRAI and SynOmics offer tailored solutions for particular biomedical challenges, with GNNRAI focusing on explainable integration of multi-omics data with biological priors for biomarker discovery [19], and SynOmics specializing in feature-level integration of multi-omics data through simultaneous learning of within-omics and cross-omics dependencies [21]. These complementary approaches address different aspects of the multi-omics integration challenge, providing researchers with options suited to their specific data characteristics and research questions.

Implementation considerations and future directions

Successful implementation of graph-based approaches in biomedical research requires careful consideration of both computational and biological factors. Key implementation challenges include the high dimensionality of omics data, limited sample sizes typical of biomedical studies, missing data across modalities, and the need for biological interpretability in addition to predictive accuracy. The research reagents and frameworks discussed address these challenges through various strategies, including dimensionality reduction techniques, transfer learning approaches, specialized architectures for handling missing data, and explainability methods tailored to biological domains.

Future directions in graph-based biomedical research include increased focus on multimodal AI integration combining genomic, proteomic, imaging, and clinical data; development of more sophisticated explainable AI (XAI) methods that provide biologically meaningful insights; emergence of foundation models for biology pre-trained on large-scale molecular data; and advancement of automated hypothesis generation systems that leverage graph structures to propose novel research directions [23]. These developments promise to further enhance the utility of graph-based approaches for tackling the complex challenges of biomedical research and drug development.

The comprehensive benchmarking of graph neural networks against alternative machine learning approaches across diverse biomedical data types reveals a consistent pattern: GNNs achieve state-of-the-art performance when relational structures and interactions between biological entities provide critical information for prediction. This advantage is most pronounced for molecular structures, knowledge graphs incorporating biological priors, patient similarity networks, and multi-omics interactions—precisely those domains where conventional machine learning approaches struggle to capture the complex dependencies inherent in biological systems.

The experimental evidence from rigorous benchmarking studies indicates that while optimal GNN architectures vary by application domain, attention-based mechanisms like GATs consistently demonstrate strong performance across tasks, particularly for heterogeneous data where the relevance of different relationships varies substantially. As the field advances, increasing integration of biological domain knowledge with flexible data-driven learning appears to be the most promising path forward, balancing the mechanistic insights from established biological knowledge with the pattern recognition power of modern deep learning approaches.

GNNs in Action: Methodological Approaches and Cutting-Edge Biomedical Applications

Molecular Property Prediction and De Novo Drug Design with GNNs

Graph Neural Networks (GNNs) have emerged as transformative tools in computational drug discovery, revolutionizing how researchers approach molecular property prediction and de novo molecular design [9]. By natively representing molecules as graphs with atoms as nodes and bonds as edges, GNNs inherently capture the structural relationships that define chemical properties and functions [24]. This representation enables accurate modeling of molecular interactions with binding targets, significantly accelerating early-stage drug discovery processes [9].

The integration of GNNs into biomedical research pipelines represents a paradigm shift from traditional descriptor-based machine learning methods. Whereas conventional approaches relied on hand-crafted molecular features, GNNs automatically learn task-specific representations through message-passing mechanisms that aggregate information from neighboring atoms across the molecular graph [25]. This review provides a comprehensive benchmarking analysis of GNN performance against alternative machine learning methods, examining predictive accuracy, computational efficiency, and practical applicability across key drug discovery tasks.

Performance Benchmarking: GNNs vs. Alternative Approaches

Molecular Property Prediction Accuracy

Molecular property prediction serves as a cornerstone of computational drug discovery, enabling researchers to identify promising candidates for expensive experimental validation. Benchmarking studies comprehensively evaluate performance across diverse chemical endpoints, from quantum mechanical properties to physiological characteristics.

Table 1: Performance Comparison Across Molecular Property Prediction Models

Model Category	Specific Models	Key Strengths	Performance Notes	Best-Suited Tasks
Descriptor-Based ML	SVM, XGBoost, Random Forest (RF)	Excellent computational efficiency; Strong interpretability; Reliable for small datasets	Outperforms graph-based models on average for prediction accuracy; SVM excels in regression tasks; RF/XGBoost strong for classification [25]	Classical QSAR tasks; Resource-constrained environments; Rapid screening pipelines
Graph Neural Networks	GCN, GAT, MPNN, Attentive FP	Automatic feature learning; Structure-aware representations; State-of-the-art on some benchmarks	Attentive FP achieves best predictions on 6/11 MoleculeNet benchmarks; Excels on larger/multi-task datasets [25]	Large-scale multi-task prediction; Complex structure-property relationships
Advanced GNN Variants	KA-GNN, Fourier-KAN, Quantized GNN	Enhanced expressivity; Parameter efficiency; Improved interpretability	KA-GNNs consistently outperform conventional GNNs in accuracy and efficiency [10]; Quantization maintains performance with reduced footprint [26]	High-precision prediction tasks; Resource-constrained deployment

Experimental data from comparative studies reveals nuanced performance patterns. A comprehensive evaluation across 11 public datasets demonstrated that descriptor-based models using SVM, XGBoost, and Random Forest algorithms generally outperformed graph-based models in both prediction accuracy and computational efficiency for many standard tasks [25]. SVM consistently achieved the best performance for regression tasks, while Random Forest and XGBoost provided reliable classification accuracy [25].

However, certain GNN architectures demonstrated exceptional capabilities on specific problem types. The Attentive FP model yielded state-of-the-art performance on 6 out of 11 MoleculeNet benchmark datasets, including both regression (ESOL, FreeSolv) and classification (MUV, BBBP, ToxCast, ClinTox) tasks [25]. This suggests that GNNs particularly excel when processing larger datasets or multi-task learning scenarios where their capacity to learn complex structural representations provides substantive advantages.

Computational Efficiency and Deployment Considerations

Computational requirements present practical considerations for model selection in research environments. Benchmarking analyses reveal significant disparities in training time and resource consumption across model classes.

Table 2: Computational Efficiency Comparison Across Model Types

Model Type	Training Time	Memory Requirements	Inference Speed	Hardware Considerations
Tree-Based Methods (XGBoost, RF)	Seconds to minutes for large datasets [25]	Low memory footprint	Extremely fast prediction	CPU-optimized; Minimal hardware requirements
Descriptor-Based DNN	Moderate training time	Moderate memory needs	Fast inference	Standard GPU beneficial but not required
Standard GNNs (GCN, GAT)	Hours for large datasets	High memory consumption	Moderate inference speed	GPU acceleration essential for practical use
Quantized GNNs (INT8)	Similar training time to standard GNNs	4x memory reduction [26]	2-3x speedup over FP32 [26]	Mobile/edge device deployment possible

Descriptor-based models employing XGBoost and Random Forest algorithms demonstrate exceptional computational efficiency, often requiring only seconds to train models even for large datasets [25]. This efficiency advantage makes them particularly suitable for rapid prototyping and resource-constrained environments.

In contrast, GNNs typically demand substantial computational resources for training, with high memory footprint and longer training times [25] [26]. However, recent advancements in model optimization have begun addressing these limitations. Quantization techniques that represent model parameters in fewer bits can significantly reduce memory requirements and computational costs while maintaining predictive performance [26]. For instance, 8-bit quantization maintains strong performance on quantum mechanical property prediction tasks, with some architectures showing minimal performance degradation despite 4x memory reduction [26].

Experimental Protocols and Methodologies

Benchmarking Framework Design

Robust benchmarking of molecular property prediction models requires standardized evaluation frameworks to ensure fair comparison across methodologies. The MoleculeNet benchmark provides a widely-adopted foundation comprising diverse datasets spanning quantum mechanics, physical chemistry, biophysics, and physiology [25] [26]. Recommended experimental protocols include:

Dataset Curation and Partitioning: Studies should employ standardized data splits (typically 80%/10%/10% for training/validation/testing) with stratification to maintain distribution consistency [25] [26]. For the ToxCast multi-task dataset, exclusion of highly imbalanced subdatasets (class ratio >50 or compounds <500) ensures meaningful evaluation [25].

Molecular Representation Standards:

• Descriptor-based models: Combine 206 MOE 1-D/2-D descriptors with 881 PubChem fingerprints and 307 substructure fingerprints for comprehensive feature coverage [25].
• Graph-based models: Use molecular graphs with atom-level features (atom type, hybridization, valence) and bond-level features (bond type, conjugation) [25].

Evaluation Metrics:

• Regression tasks: Root Mean Square Error (RMSE), Mean Absolute Error (MAE)
• Classification tasks: Area Under Precision-Recall Curve (AUPR), ROC-AUC
• Multi-task benchmarks: Aggregate metrics across all tasks

GNN-Specific Methodological Considerations

Architecture Selection: Comparative studies should include diverse GNN architectures covering convolutional (GCN), attention-based (GAT), message-passing (MPNN), and advanced variants (Attentive FP) [25]. Recent innovations such as Kolmogorov-Arnold GNNs (KA-GNNs) that integrate Fourier-based univariate functions demonstrate enhanced expressivity and parameter efficiency [10].

Training Protocols:

• Implementation: PyTorch Geometric or Deep Graph Library
• Optimization: Adam optimizer with learning rate 0.001-0.0001
• Regularization: Early stopping with patience 50-100 epochs
• Hyperparameter tuning: Grid search for layer depth (2-6), hidden dimensions (64-512), dropout rate (0.0-0.5)

Advanced GNN training incorporates innovative approaches such as gradient ascent-based inversion, where molecular graphs are optimized against pre-trained property predictors to generate structures with desired characteristics [27]. This methodology enables de novo molecular design without additional training on structural data.

Validation and Interpretation Methods

Robust model validation extends beyond standard performance metrics to include interpretability analyses and experimental confirmation:

Interpretability Techniques: SHAP (SHapley Additive exPlanations) analysis effectively identifies important molecular descriptors and structural features learned by prediction models [25]. For GNNs, attention mechanisms and saliency maps highlight chemically meaningful substructures contributing to predictions [10].

Experimental Confirmation: For de novo molecular design, computational predictions require experimental validation. Generated molecules targeting specific HOMO-LUMO gaps should undergo density functional theory (DFT) verification to confirm predicted electronic properties [27]. Studies demonstrate that while GNN proxies successfully generate molecules with requested properties, the performance gap between proxy predictions and DFT confirmation highlights the importance of physical validation [27].

Emerging Architectures and Specialized Applications

Advanced GNN Architectures

Recent GNN innovations address specific limitations in molecular modeling:

Kolmogorov-Arnold GNNs (KA-GNNs): By integrating Fourier-based univariate functions into node embedding, message passing, and readout components, KA-GNNs achieve superior accuracy and computational efficiency compared to conventional GNNs [10]. These architectures demonstrate enhanced interpretability by highlighting chemically meaningful substructures relevant to property prediction [10].

Causal Graph Neural Networks (CIGNNs): Moving beyond correlation-based prediction, CIGNNs incorporate causal inference principles to learn invariant biological mechanisms rather than spurious correlations [28]. This approach addresses critical challenges in healthcare deployment, including distribution shift, discrimination, and interpretability limitations [28].

Quantized GNNs: Employing reduced-precision arithmetic through techniques like the DoReFa-Net algorithm, quantized GNNs maintain predictive performance while significantly reducing memory footprint and computational demands [26]. This enables deployment on resource-constrained devices without substantial accuracy degradation at 8-bit precision [26].

Specialized Applications in Drug Discovery

Spatial Molecular Profiling: GNNs applied to spatial omics data model tissue architecture by representing cells as nodes and spatial proximity as edges [8]. While incorporating spatial context does not always enhance classification performance for simple phenotypes, GNNs capture biologically meaningful features and reveal disease-relevant tissue organization patterns [8].

Multi-Scale Modeling: Advanced frameworks integrate molecular-level GNN predictions with higher-order biological systems, enabling in silico clinical experimentation through patient-specific Causal Digital Twins [28]. These systems simulate intervention effects across biological scales before clinical application [28].

Research Reagent Solutions: Essential Tools for Implementation

Table 3: Essential Research Tools for GNN Implementation in Drug Discovery

Tool Category	Specific Solutions	Key Functionality	Application Context
Deep Learning Frameworks	PyTorch Geometric, Deep Graph Library, TensorFlow	GNN model implementation; Molecular graph processing; Batch processing for variable-sized graphs	Core model development; Experimental prototyping; Production deployment
Cheminformatics Libraries	RDKit, Open Babel	Molecular graph generation from SMILES; Descriptor calculation; Fingerprint generation	Data preprocessing; Feature engineering; Molecular validity checks
Benchmark Datasets	MoleculeNet (ESOL, FreeSolv, Lipophilicity, QM9, Tox21)	Standardized benchmarking; Performance comparison across methods	Model evaluation; Comparative studies; Methodological validation
Specialized Architectures	Attentive FP, KA-GNN, D-MPNN	State-of-the-art performance; Enhanced interpretability; Specialized message passing	Advanced research; High-precision prediction tasks; Interpretable AI requirements
Optimization Tools	DoReFa-Net, Quantization Aware Training	Model compression; Inference acceleration; Memory footprint reduction	Resource-constrained deployment; Mobile health applications; High-throughput screening

Benchmarking analyses reveal that the choice between GNNs and alternative machine learning methods for molecular property prediction depends critically on specific research constraints and objectives. Descriptor-based models employing SVM, XGBoost, and Random Forest algorithms provide compelling advantages for standard prediction tasks where computational efficiency and interpretability are prioritized [25]. However, GNNs demonstrate superior capabilities for complex structure-property relationships, multi-task learning scenarios, and de novo molecular design [9] [27].

Future research directions focus on enhancing GNN capabilities while addressing current limitations. Emerging priorities include developing more sample-efficient architectures that maintain performance with limited training data, improving interpretability to build trust in predictive outputs, and enhancing integration with experimental validation pipelines [29]. The convergence of GNNs with causal inference frameworks represents a particularly promising direction, enabling robust prediction under distribution shift and facilitating reliable treatment effect estimation [28].

As the field advances, the complementary strengths of descriptor-based and graph-based approaches suggest opportunities for hybrid frameworks that leverage the efficiency of traditional machine learning with the representational power of GNNs. Such integrated approaches promise to further accelerate drug discovery by combining methodological strengths while mitigating their respective limitations.

The accurate prediction of critical clinical events like sepsis and mortality is a paramount challenge in modern healthcare. The proliferation of Electronic Health Records (EHRs) has created unprecedented opportunities for predictive modeling, yet the choice of analytical methodology profoundly impacts clinical utility. Within the specific context of benchmarking graph neural networks (GNNs) against other machine learning (ML) methods for biomedical data research, a clear performance landscape is emerging. Traditional ML models and scoring systems have long been the standard bearers, but novel approaches leveraging patient similarity graphs and advanced neural architectures are demonstrating significant advantages in capturing the complex, relational nature of clinical data. This guide provides a comparative analysis of these methodologies, detailing their experimental protocols, performance metrics, and essential components to inform researchers and drug development professionals.

Performance Benchmarking: Quantitative Comparative Analysis

The table below summarizes the reported performance of various model architectures on key clinical prediction tasks, providing a direct comparison of their predictive capabilities.

Table 1: Performance Benchmarking of Clinical Prediction Models

Model Category	Specific Model/Approach	Prediction Task	Dataset(s)	Key Performance Metric(s)	Reported Performance
Graph Neural Networks	HybridGraphMedGNN (GCN, GraphSAGE, GAT) [30]	ICU Mortality	MIMIC-III (6,000 stays)	AUC-ROC	0.94
	Similarity-Based Self-Construct Graph Model (SBSCGM) [30]	Patient Criticalness	MIMIC-III	AUC-ROC	0.94
	GCN2 (on molecular networks) [2]	Cancer-Driver Genes	STRING, BioGRID	Balanced Accuracy (BACC)	0.807 +/- 0.035
Traditional Machine Learning	LASSO Regression Model [31]	28-day Mortality (Elderly Sepsis)	Single-Center (180 patients)	AUCSensitivitySpecificity	0.84575.9%85.0%
	Point System Model [32]	28-day Mortality (Sepsis)	Multi-Center (9,720 patients)	AUC (Community-Acquired)AUC (Hospital-Acquired)	0.7870.729
	Real-Time Dynamic Model [33]	Sepsis Risk	MIMIC-IV	AUC	0.76
Scoring Systems (Baseline)	SAPS 3 [32]	28-day Mortality (Critically Ill Sepsis)	Multi-Center	AUC	0.722
	New Clinical Point System [32]	28-day Mortality (Sepsis)	Multi-Center	AUC	0.745

Detailed Experimental Protocols and Workflows

Protocol 1: GNNs for ICU Mortality Prediction

A leading approach for GNN-based mortality prediction involves the Similarity-Based Self-Construct Graph Model (SBSCGM) and a hybrid GNN architecture [30].

• Graph Construction: The model constructs a patient similarity graph ( G=(V,E) ) where each node ( v \in V ) represents a patient. Edges are formed based on a hybrid similarity score ( S(u,v) = \alpha \cdot S{feat}(u,v) + (1-\alpha) \cdot S{struct}(u,v) ), where ( S{feat} ) is cosine similarity on continuous features, ( S{struct} ) is the Jaccard index on categorical attributes, and ( \alpha=0.7 ). An edge ( (u,v) ) is created if ( S(u,v) > \tau ), with ( \tau ) set near the 90th percentile of all pairwise similarities [30].
• Node Feature Encoding: Each patient node is represented by a 133-dimensional feature vector encompassing demographics (age, gender), comorbidities (Charlson Index, ICD-9 codes), aggregated statistics (mean, min, max) of time-series vitals and labs (heart rate, creatinine, lactate), and intervention flags (ventilation, dialysis) [30].
• GNN Architecture and Training: The HybridGraphMedGNN integrates GCN, GraphSAGE, and GAT layers. Models are typically configured as two-layer architectures trained with standardized hyperparameters: dropout=0.2, Adam optimizer with learning rate=0.01, and an adjusted binary cross-entropy loss to handle class imbalance. Training often employs an 80/20 train-test split over multiple epochs with several independent runs to ensure statistical robustness [2] [30].

Diagram 1: GNN Workflow for Clinical Predictions

Protocol 2: Traditional ML for Sepsis Mortality Prediction

Traditional ML models offer a strong, often more interpretable, baseline for comparison.

• Data Collection and Preprocessing: A typical study, such as the one by [31], collects data on patient demographics, vital signs (MAP, HR), laboratory parameters (PCT, ALB, VEGF), disease-related scores (SOFA, APACHE II), comorbidities, and treatments. The key preprocessing challenges identified for structured EHR data include gathering and integrating data, handling different feature types, and addressing data missingness, often tackled via clinical knowledge-guided imputation [34] [31].
• Feature Selection and Model Building: Variables are first analyzed using univariate analysis to identify factors with significant differences between survivor and non-survivor groups. Significant variables (e.g., SOFA, APACHE II, MAP, ALB, PCT, LTB, VEGF) are then further refined using LASSO regression to prevent overfitting and select the most predictive features for the final model [31].
• Model Validation: The model undergoes internal validation using bootstrap resampling (e.g., 1000 repetitions) to correct for over-optimism and generate a validated AUC. Calibration curves are plotted to assess the agreement between predicted probabilities and actual outcomes [31] [32].

Diagram 2: Traditional ML Modeling Workflow

Successful development and benchmarking of clinical prediction models require a curated set of data, software, and computational resources.

Table 2: Essential Research Reagents & Resources for Clinical Prediction Modeling

Category	Item	Specific Examples	Function & Application
Public Data Repositories	Critical Care Databases	MIMIC-III, MIMIC-IV, eICU [35] [33]	Provide large-scale, de-identified ICU patient data for model training and validation.
	Molecular & Protein Networks	STRING, BioGRID [2]	Source for constructing biological networks in GNN models for tasks like cancer-driver gene identification.
Benchmarking Frameworks	GNN Benchmarking Suites	GNN-Suite [2]	Standardized frameworks for fair comparison of GNN architectures (e.g., GAT, GCN, GraphSAGE) using robust workflows like Nextflow.
Modeling Algorithms	Graph Neural Networks	GCN, GAT, GraphSAGE, HybridGraphMedGNN [2] [30]	Learn from graph-structured data to capture complex patient relationships and similarities.
	Traditional ML Models	LASSO Regression, Random Forest, Gradient Boosting [31] [32]	Provide strong, interpretable baselines for predictive tasks, often using selected clinical variables.
Explainability (XAI) Tools	Feature & Graph Attribution	SHAP, TreeSHAP, GNNExplainer [33] [36]	Uncover model decision logic, enhance trust, and provide potential physiological insights.
Software & Libraries	Statistical Computing	R Software [31]	Used for statistical analysis, traditional model development, and creating nomograms.
	Deep Learning Frameworks	PyTorch, TensorFlow [30]	Essential for implementing and training complex deep learning models like GNNs and Transformers.

Discussion and Future Directions

The comparative data indicates that GNN architectures, particularly those leveraging patient similarity graphs, can achieve state-of-the-art performance (AUC ~0.94) on well-defined tasks like ICU mortality prediction, outperforming many traditional ML models [30]. However, traditional ML and even simplified point-based systems remain highly competitive, especially in multi-center validation studies for sepsis mortality, with AUCs often ranging from 0.75 to 0.85 [31] [32]. Their strengths lie in interpretability and lower computational cost. A significant challenge for GNNs is the "black box" problem, which is being addressed through Explainable AI (XAI) methods. Quantitative benchmarks for evaluating XAI methods on GNNs are now emerging, allowing researchers to compare the explanations generated by AI against known ground-truth substructures or the judgments of human experts [36].

Future research will likely focus on the fusion of these methodologies. Key trends include dynamic graph construction that updates in real-time as patient conditions evolve [30], the integration of multi-modal data (structured EHR, clinical notes, molecular data) [30] [37], and the development of time-aware models that explicitly account for irregular temporal intervals between clinical events [38]. The ultimate goal is a new generation of robust, interpretable, and clinically actionable AI tools that can be seamlessly integrated into diverse healthcare environments to improve patient outcomes.

Multi-omics Data Integration for Cancer Classification and Subtype Identification

Next-generation cancer research is increasingly moving towards the full integration of big data and machine learning approaches, with graph neural networks (GNNs) emerging as powerful tools for analyzing multimodal structured information [39]. The complex heterogeneity of cancer necessitates precise molecular subtyping for accurate diagnosis, prognosis, and treatment selection. Traditional single-omics analyses often fail to capture the complete biological complexity of tumors, driving the need for sophisticated multi-omics integration approaches [40] [41].

This benchmarking guide provides a comprehensive comparison of computational methods for multi-omics data integration, with a specialized focus on evaluating graph neural networks against other machine learning frameworks. We objectively assess performance metrics, experimental methodologies, and technical requirements to guide researchers and clinicians in selecting appropriate tools for cancer classification and subtype identification.

Performance Benchmarking of Multi-omics Integration Methods

Comparative Performance Across Methodologies

Table 1: Performance comparison of multi-omics integration methods for cancer classification

Method Category	Specific Method	Cancer Types	Classification Accuracy	Key Strengths	Limitations
Graph Neural Networks	LASSO-MOGAT [20]	31 cancer types	95.9%	Best overall performance; attention mechanism	Requires substantial computational resources
	LASSO-MOGCN [20]	31 cancer types	94.88%	Effective neighborhood aggregation	Fixed graph structure limitations
	LASSO-MOGTN [20]	31 cancer types	95.67%	Handles long-range dependencies	Complex architecture; longer training time
	AMOGEL [42]	BRCA, KIPAN	State-of-the-art AUC/F1	Integrates association rule mining	Computationally intensive for large datasets
	GNN (Bladder Cancer) [43]	Bladder cancer	AUC: 0.839	Pathway-based topological features	Limited to specific cancer type
Deep Learning (Non-GNN)	Biologically Explainable AI [40]	30 cancer types	96.67%	Explainable feature selection	Complex pipeline implementation
	MOCAT [42]	BRCA subtypes	Not specified	Multi-head attention mechanism	Requires precise hyperparameter tuning
Statistical Integration	MOFA+ [41]	Breast cancer	F1-score: 0.75	Interpretable factors; handles missing data	Limited predictive performance vs. DL
	MOGCN [41]	Breast cancer	Lower than MOFA+	Non-linear relationships	Underperformed in feature selection

Multi-omics Integration Performance by Data Type

Table 2: Performance comparison by omics data types integrated

Method	mRNA Alone	miRNA Alone	Methylation Alone	mRNA + miRNA	All Three Omics
LASSO-MOGAT [20]	95.02%	94.11%	94.88%	95.45%	95.90%
LASSO-MOGCN [20]	94.21%	93.67%	93.92%	94.78%	94.88%
LASSO-MOGTN [20]	94.85%	93.98%	94.25%	95.22%	95.67%
Biologically Explainable AI [40]	Not reported	Not reported	Not reported	Not reported	96.67% (external validation)

Methodological Approaches and Experimental Protocols

Graph Neural Network Architectures

GNNs have emerged as particularly effective for multi-omics integration due to their ability to model complex biological relationships as graph structures [39]. The fundamental operation of GNNs involves message passing between nodes, where each node updates its representation by aggregating information from its neighbors [39]. Three predominant architectures have been benchmarked:

Graph Convolutional Networks (GCNs) operate by applying convolutional operations to graph-structured data, enabling nodes to learn representations based on their local neighborhoods [20]. In multi-omics applications, GCNs typically represent patients as nodes and similarities between patients as edges.

Graph Attention Networks (GATs) incorporate attention mechanisms that assign varying weights to neighboring nodes, allowing the model to focus on more relevant connections [20]. This is particularly valuable in biological systems where certain molecular interactions have greater functional significance.

Graph Transformer Networks (GTNs) extend the transformer architecture to graph structures, enabling the modeling of long-range dependencies across the graph [20]. This capability is beneficial for capturing complex genomic interactions that may not be immediately adjacent in biological networks.

Feature Selection and Biological Explainability

A critical challenge in multi-omics analysis is the high dimensionality of data, where feature selection methods play a crucial role in model performance. The biologically explainable AI framework [40] employs a hybrid feature selection approach combining gene set enrichment analysis (GSEA) and Cox regression to identify cancer-associated features in transcriptome, methylome, and microRNA datasets. This method specifically selects genes involved in molecular functions, biological processes, and cellular components (p < 0.05), then subjects them to univariate Cox regression analysis to identify genes linked with cancer patient survival [40].

LASSO-based approaches implement feature selection through L1 regularization, which effectively reduces the feature space by forcing less important coefficients to zero [20]. The AMOGEL framework incorporates association rule mining (ARM) to discover intra-omics and inter-omics relationships, forming a multi-omics synthetic information graph before model training [42].

Experimental Workflows and Data Processing

A standardized benchmarking pipeline for multi-omics integration typically involves several critical stages. For synthetic lethality prediction, comprehensive assessment includes three data splitting methods (CV1, CV2, CV3) with increasing difficulty levels, four positive-to-negative ratios (1:1, 1:5, 1:20, 1:50), and three negative sampling methods (random, expression-based, dependency-based) [44].

The following diagram illustrates a typical experimental workflow for multi-omics data integration using graph neural networks:

Multi-omics Integration Workflow

Technical Implementation and Resource Requirements

Computational Resource Demands

The computational requirements for multi-omics integration methods vary significantly based on the approach and scale of data. GNN-based methods generally demand substantial resources, with models like MOGAT requiring eight NVIDIA A100 GPUs with 40GB of GPU memory each when integrating eight omics types [42]. The AMOGEL framework with association rule mining also presents computational challenges for large datasets due to the combinatorial nature of rule discovery [42].

In contrast, statistical approaches like MOFA+ demonstrate more modest computational requirements, making them accessible for researchers with limited hardware resources [41]. However, this advantage comes at the cost of reduced predictive performance compared to deep learning methods.

Data Requirements and Input Specifications

Multi-omics integration methods utilize diverse data types and structures. The following researcher's toolkit table summarizes key computational reagents and their functions:

Table 3: Research reagent solutions for multi-omics integration

Resource Type	Specific Examples	Function in Analysis	Implementation Considerations
Biological Networks	Protein-protein interactions (BioGRID) [44]	Prior knowledge for graph construction	Quality depends on completeness of knowledge [42]
	KEGG Pathways [44]	Pathway enrichment analysis	Curated pathways enhance biological relevance
	Gene Ontology [44]	Functional annotation	Provides standardized gene functions
Data Resources	TCGA (The Cancer Genome Atlas) [40] [43]	Multi-omics data source	Standardized cohort with multiple omics layers
	SynLethDB [44]	Synthetic lethality database	Gold standard for SL interactions
	GEO Datasets [45]	Independent validation data	Essential for external validation
Software Tools	MOVICS [45]	Multi-omics clustering integration	Integrates 10 clustering algorithms
	PyTorch Geometric [43]	GNN implementation	Specialized library for graph deep learning
	Captum [43]	Model interpretability	IG algorithm for feature importance

Validation Frameworks and Real-World Performance

Validation Methodologies

Robust validation is essential for assessing model performance and generalizability. The biologically explainable AI framework [40] employed external dataset validation, achieving 96.67% accuracy for tissue-of-origin classification across 30 cancer types. For subtype identification, the model demonstrated accuracies ranging from 87.31% to 94.0%, while stage classification achieved 83.33% to 93.64% accuracy [40].

The synthetic lethality benchmarking study [44] implemented three distinct cross-validation strategies with increasing difficulty: CV1 (random split), CV2 (semi-cold start with one gene unseen), and CV3 (cold start with both genes unseen). This progressive approach provides realistic assessment of model generalizability to novel genes not present in training data.

Clinical Translation and Applications

Several studies demonstrate promising clinical applications of multi-omics integration. For breast cancer subtyping, the MammaPrint and BluePrint assays provide real-world clinical implementation of genomic testing, with the FLEX study enrolling over 20,000 patients to validate utility across diverse populations [46]. These assays successfully identify distinct molecular subtypes (Luminal-type, HER2-type, Basal-type) that warrant different treatment pathways [46].

In bladder cancer, a GNN model successfully predicted immunotherapy response with AUC of 0.839 on the validation set, identifying key pathways and generating a responseScore that correlated with immune cell infiltration and anti-tumor immunity [43]. Single-cell analysis further revealed that the score was closely related to the functional state of natural killer cells [43].

The following diagram illustrates the relationship between multi-omics features and clinical applications in cancer research:

Multi-omics Clinical Applications

Based on comprehensive benchmarking across multiple studies, graph neural networks consistently demonstrate superior performance for multi-omics integration in cancer classification and subtype identification. The attention mechanism in GAT architectures provides particular advantages for biological data where certain molecular interactions have greater functional significance [20].

For researchers with sufficient computational resources, GNN-based approaches like LASSO-MOGAT and AMOGEL offer state-of-the-art performance [42] [20]. When biological interpretability is prioritized, frameworks incorporating explainable AI principles and pathway analysis provide valuable insights into molecular mechanisms [40] [43]. In resource-constrained environments or for initial exploratory analysis, statistical methods like MOFA+ offer a accessible entry point with reasonable performance [41].

Future development should address computational efficiency challenges and improve model interpretability for clinical translation. The integration of prior biological knowledge with data-driven approaches represents a promising direction for enhancing both performance and biological relevance of multi-omics integration models.

Pre-training on biomedical knowledge graphs for downstream task performance enhancement

In biomedical data science, graph neural networks (GNNs) have emerged as powerful tools for analyzing complex biological relationships represented as knowledge graphs (KGs). These graphs structure biomedical concepts as nodes and their relationships as edges, creating rich networks of domain knowledge. Pre-training GNNs on these structured knowledge sources has become a pivotal strategy for enhancing performance on downstream predictive tasks including drug discovery, disease association prediction, and biological interaction forecasting. This guide compares prominent biomedical KG pre-training frameworks, analyzes their experimental performance against alternatives, and situates these findings within the broader context of benchmarking GNNs against other machine learning methods for biomedical data research.

Comparative Framework Analysis

Multiple research groups have developed specialized frameworks for pre-training GNNs on biomedical knowledge graphs, each employing distinct architectural strategies and optimization techniques.

Table 1: Overview of Biomedical KG Pre-training Frameworks

Framework	Pre-training Strategy	KG Sources	Target Downstream Tasks	Key Innovations
PT-KGNN [47]	Multi-scale KG pre-training	Large-scale biomedical KGs	Drug-drug interaction (DDI), Drug-disease association (DDA)	Scale-aware pre-training demonstrating performance improvements with larger KGs
LukePi [48]	Self-supervised learning with dual tasks	Biomedical KGs	Synthetic lethality, Drug-target interactions	Combines topology-based node degree classification and semantics-based edge recovery
BALI [49]	Cross-modal LM-KG alignment	UMLS	Question answering, Entity linking	Aligns language model representations with KG embeddings using contrastive learning
GNN-Suite [2]	Standardized benchmarking	STRING, BioGRID, PCAWG, PID, COSMIC-CGC	Cancer-driver gene identification	Modular framework for fair GNN architecture comparison

PT-KGNN Framework

PT-KGNN applies pre-training techniques inspired by natural language processing to biomedical knowledge graphs, learning comprehensive node embeddings through graph neural networks. The framework's core innovation lies in its systematic demonstration that downstream task performance consistently improves as the scale of the biomedical KG used for pre-training increases [47]. This scale-aware approach significantly enhances drug-drug interaction (DDI) and drug-disease association (DDA) prediction performance on independent datasets, with embeddings derived from larger biomedical KGs demonstrating superior performance compared to those from smaller KGs [47].

LukePi Framework

LukePi employs a novel self-supervised pre-training approach that combines two complementary tasks: topology-based node degree classification and semantics-based edge recovery [48]. This dual-task strategy enables the model to capture both structural patterns and semantic relationships within biomedical knowledge graphs. The framework specifically addresses challenges of distribution shifts between training and test data and low-data scenarios common in biomedical research, where labeling interactions is time-consuming and labor-intensive. Evaluations on synthetic lethality and drug-target interaction prediction tasks demonstrate that LukePi significantly outperforms 22 baseline models [48].

BALI Framework

BALI (Biomedical Knowledge Graph and Language Model Alignment) introduces a joint pre-training method that enhances language models with external knowledge by simultaneously learning a dedicated KG encoder and aligning the representations of both the language model and the graph [49]. For a given textual sequence, the framework links biomedical concept mentions to the Unified Medical Language System (UMLS) KG and utilizes local KG subgraphs as cross-modal positive samples for these mentions. This approach improves performance on language understanding tasks and enhances the quality of entity representations, even with minimal pre-training on small alignment datasets sourced from PubMed scientific abstracts [49].

Experimental Performance Comparison

Rigorous benchmarking provides critical insights into the relative performance of KG pre-training approaches compared to traditional methods and their effectiveness across diverse biomedical prediction tasks.

Table 2: Quantitative Performance Comparison Across Frameworks and Tasks

Framework/Task	Metric	Performance	Baseline Comparison	Dataset
PT-KGNN [47]	Prediction accuracy	Consistent improvement with KG scale	Outperforms non-pre-trained models	DDI, DDA benchmarks
LukePi [48]	Link prediction accuracy	Significant improvement over baselines	Outperforms 22 baseline models	Synthetic lethality, Drug-target interactions
BALI [49]	Question answering accuracy	+2.1% PubMedQA, +1.7% MedQA, +6.2% BioASQ	Outperforms BioLinkBERT, PubMedBERT	PubMedQA, MedQA, BioASQ
GNN-Suite [2]	Balanced accuracy (BACC)	0.807 +/- 0.035 (GCN2)	All GNNs outperform logistic regression baseline	STRING-based molecular networks
ComplEx [50]	HITS@10	0.793	Best-performing KGE model on BioKG	BioKG link prediction

Performance on Specific Biomedical Tasks

Drug-Drug and Drug-Disease Interaction Prediction

PT-KGNN demonstrates that pre-training on large-scale biomedical KGs substantially improves prediction of drug-drug interactions (DDI) and drug-disease associations (DDA) on independent validation datasets [47]. The embeddings learned from larger knowledge graphs consistently yield superior performance, highlighting the value of comprehensive biomedical knowledge coverage. Similarly, LukePi shows marked improvements in predicting drug-target interactions, particularly in challenging low-data scenarios where traditional supervised approaches struggle [48].

Biomedical Question Answering and Entity Linking

The BALI framework achieves significant accuracy improvements on standard biomedical question answering benchmarks, including gains of 2.1% on PubMedQA, 1.7% on MedQA, and 6.2% on BioASQ compared to strong baselines like PubMedBERT and BioLinkBERT [49]. This demonstrates the value of cross-modal alignment between language representations and structured knowledge graphs for complex reasoning tasks in the biomedical domain.

Cancer-Driver Gene Identification

In the GNN-Suite benchmarking framework, GCN2 achieves the highest balanced accuracy (0.807 +/- 0.035) on STRING-based molecular networks for identifying cancer-driver genes [2]. Importantly, all evaluated GNN architectures (GAT, GCN, GIN, GraphSAGE, etc.) consistently outperformed logistic regression baselines, demonstrating the advantage of network-based learning over feature-only approaches for this critical biomedical prediction task [2].

Methodological Approaches

Experimental Protocols

GNN-Suite Benchmarking Methodology

The GNN-Suite framework employs strict standardization to ensure fair comparisons among diverse GNN architectures including GAT, GCN, GIN, and GraphSAGE alongside logistic regression baselines [2]. All GNNs are configured as standardized two-layer models trained with uniform hyperparameters: dropout rate of 0.2, Adam optimizer with learning rate of 0.01, and adjusted binary cross-entropy loss to address class imbalance [2]. Models are evaluated using an 80/20 train-test split over 300 epochs, with each model undergoing 10 independent runs with different random seeds to yield statistically robust performance metrics, using balanced accuracy (BACC) as the primary evaluation measure [2].

Knowledge Graph Embedding Evaluation

For knowledge graph embedding methods, standard evaluation protocols include metrics such as HITS@10 and Mean Reciprocal Rank (MRR) for link prediction tasks [50]. The ComplEx model emerges as the best-performing KGE approach on the BioKG knowledge graph, achieving a HITS@10 score of 0.793 and an MRR of 0.629 [50]. Tensor factorization models generally outperform other approaches, suggesting that similarity-based scoring functions are particularly well-suited for biomedical knowledge graphs.

Cross-Modal Alignment Strategy

BALI's cross-modal alignment approach utilizes a graph neural network to capture and encode graph knowledge into node embeddings, while a pre-trained language model generates textual entity representations [49]. These representations serve as anchors to align the two uni-modal embedding spaces, creating a shared representation that enhances performance on downstream biomedical NLP tasks.

Workflow Visualization

Diagram 1: BALI Framework Pre-training and Fine-tuning Workflow [49]

Diagram 2: LukePi Dual-Task Self-Supervised Learning Architecture [48]

The Scientist's Toolkit

Table 3: Essential Research Reagents and Computational Resources

Resource	Type	Function	Application Examples
UMLS [49]	Knowledge Graph	Comprehensive biomedical concept repository	Entity linking, Relation extraction
BioKG [50]	Knowledge Graph	Multi-source biomedical entity relationships	Drug repurposing, Side-effect prediction
STRING [2]	Protein Interaction Network	Protein-protein association data	Cancer-driver gene identification
BioGRID [2]	Biological Repository	Protein and genetic interactions	Molecular network construction
GNN-Suite [2]	Benchmarking Framework	Standardized GNN evaluation	Architecture comparison, Hyperparameter tuning
Adapter Modules [51]	Lightweight Neural Components	Knowledge injection into LMs	Parameter-efficient domain adaptation
ComplEx [50]	Embedding Model	Knowledge graph link prediction	Polypharmacy task adaptation

Pre-training graph neural networks on biomedical knowledge graphs consistently enhances performance across diverse downstream tasks including drug interaction prediction, disease association mapping, and biomedical question answering. The comparative analysis reveals that frameworks incorporating self-supervised learning objectives, cross-modal alignment strategies, and scale-aware pre-training generally outperform traditional machine learning approaches and non-pre-trained GNN models. The most effective implementations successfully address key biomedical research challenges including data scarcity, distribution shifts, and the need for model interpretability. As the field advances, standardized benchmarking frameworks like GNN-Suite will play an increasingly critical role in guiding the development of more powerful and clinically relevant predictive models for biomedical research and drug development.

Overcoming Real-World Hurdles: Troubleshooting and Optimizing GNNs for Robust Deployment

Addressing Distribution Shift and Poor Generalizability Across Institutions

The deployment of artificial intelligence (AI) in biomedical research and clinical practice is fundamentally challenged by distribution shift, a phenomenon where models trained on historical data suffer performance decay when applied to new institutions, patient populations, or evolving clinical practices. This problem of poor generalizability undermines the reliability of AI systems for critical applications including disease diagnosis, risk prediction, and treatment recommendation. Graph Neural Networks (GNNs) have emerged as a promising framework for modeling complex biomedical relationships. This guide provides an objective comparison of GNNs against traditional machine learning methods in addressing distribution shift, synthesizing experimental data and methodologies to inform researchers, scientists, and drug development professionals.

Performance Comparison: Quantitative Evidence

The following tables summarize experimental results from key studies evaluating model performance under distribution shift in biomedical applications.

Table 1: Performance comparison for axillary lymph node metastasis (ALNM) prediction in breast cancer

Model Type	AUC	Sensitivity	Specificity	Test Cohort	Key Advantage
Graph Convolutional Network (GCN)	0.77	-	-	Independent test cohort (n=118)	Best overall performance [52]
Graph Attention Network (GAT)	-	-	-	Same test cohort	Attention mechanism [52]
Graph Isomorphism Network (GIN)	-	-	-	Same test cohort	Enhanced discriminative power [52]
Traditional ML	Lower than GCN	-	-	Same test cohort	Limited structural learning [52]

Table 2: Temporal shift robustness in clinical risk prediction (heart failure and stroke)

Method	Pre-shift Performance	Post-shift Performance	Performance Drop	Shift Mitigation Approach
Standard RETAIN	High	Moderate	Significant	None [53]
Standard Dipole	High	Moderate	Significant	None [53]
Sample Reweighting + RETAIN	High	Higher than standard	Reduced	Sample reweighting + KL-divergence [53]
Sample Reweighting + Dipole	High	Higher than standard	Reduced	Sample reweighting + KL-divergence [53]

Table 3: Distribution shift detection performance for diabetic retinopathy grading

Detection Method	Shift Type	Sample Size Needed	Detection Rate	Key Limitation
Classifier-based Test (C2ST)	Patient sex, image quality, comorbidities	30,000 for sex shifts; 1,000 for quality/comorbidities	Perfect for quality/comorbidity shifts	Large sample needs for some shifts [54]
Deep Kernel Methods	Image quality, ethnicity	≤300 for easy shifts	High for easy-to-detect shifts	Limited for subtle subgroup shifts [54]
Multiple Univariate KS Tests	Various acquisition shifts	≤300 for easy shifts	Good for basic OOD detection	Unsuitable for hidden subgroup shifts [54]

Experimental Protocols and Methodologies

GNN Architecture for ALNM Prediction

The comparative analysis of GNNs for predicting axillary lymph node metastasis in breast cancer employed the following rigorous methodology [52]:

Data Composition: The study utilized a dataset of 584 women with malignant breast lesions, split into training (80%) and independent test (20%) cohorts. The dataset included axillary ultrasound findings, histopathologic data (tumor type, ER status, PR status, HER-2, Ki-67), and clinical data (age, US size, tumor location, BI-RADS category).

Graph Construction: Researchers created a feature table where each patient represented a node. They computed cosine similarity between nodes to establish edges, applying a correlation cutoff of ≥0.95 to reduce noise and redundancy. This resulted in a graph structure with nodes (patients) and edges (similarity relationships).

Model Configurations:

• GCN: Implemented graph convolutions to aggregate neighbor information and update node representations.
• GAT: Incorporated attention mechanisms to weight the importance of neighboring nodes.
• GIN: Utilized a sum aggregation function with a multi-layer perceptron for enhanced discriminative power.

Training Protocol: All models used Adam optimizer with batch size of 32, learning rate of 0.0001, and 1000 training epochs with PyTorch 2.2.2 and Keras 2.10.0 with Python 3.10.12.

Temporal Shift Mitigation in Clinical Risk Prediction

The study addressing temporal distribution shifts in electronic health records implemented this experimental approach [53]:

Data and Shift Simulation: Utilized MarketScan Commercial Claims and Encounters database with 1,178,997 patients. Treated EHRs before October 2015 (ICD-9-CM) as pre-shift data and EHRs after October 2015 (ICD-10-CM) as post-shift data, creating a natural experiment for temporal shift.

Reweighting Methodology:

• Calculated occurrence rates of medical codes in pre-shift and post-shift environments
• Applied mean squared error to directly equalize code occurrence rates between environments
• Implemented Kullback-Leibler divergence loss to force similar patient representations in both environments
• Reweighted training samples from pre-shift data to better match post-shift distribution

Evaluation Framework: Tested method on heart failure and stroke risk prediction tasks using established models (RETAIN, Dipole) with and without reweighting, measuring performance on post-shift test data.

Distribution Shift Detection in Medical Imaging

The retinal image analysis study implemented these detection protocols [54]:

Shift Simulation: Created distribution shifts by altering prevalence of patient sex, ethnicity, comorbidities, and image quality in a dataset of 130,486 retinal images.

Detection Methods:

• Classifier-based Test (C2ST): Trained a classifier to distinguish between source and target distributions
• Deep Kernel Methods (MMDD): Applied maximum mean discrepancy with deep kernels
• Multiple Univariate Kolmogorov-Smirnov Tests (MUKS): Conducted multiple hypothesis tests on feature representations

Performance Evaluation: Measured detection rates across different sample sizes (100-30,000) for each shift type, with statistical power analysis.

Visualizing Methodological Approaches

Graph 1: Comparative workflows of GNN vs. traditional ML approaches to distribution shift.

Graph 2: Sample reweighting methodology for mitigating temporal distribution shifts.

The Scientist's Toolkit: Essential Research Reagents

Table 4: Key computational reagents for distribution shift research

Tool/Resource	Type	Function	Application Example
PyTorch Geometric	Library	Graph neural network implementation	Building GCN, GAT, GIN models [52]
BioKG	Knowledge Graph	Biomedical relationship repository	Pre-training GNNs for drug discovery [7]
PrimeKG	Knowledge Graph	Precision medicine analysis	Multimodal disease relationship modeling [7]
DGL (Deep Graph Library)	Framework	Graph neural network development	Drug-drug interaction prediction [7]
MarketScan CCAE	Dataset	Longitudinal healthcare claims	Temporal shift simulation [53]
Diabetic Retinopathy Detection Dataset	Image Dataset	Retinal fundus images	Acquisition shift analysis [54]

The experimental evidence demonstrates that Graph Neural Networks, particularly when enhanced with causal frameworks and specifically designed mitigation strategies, show superior performance in addressing distribution shift and poor generalizability across institutions compared to traditional machine learning methods. The structural learning capabilities of GNNs, combined with sample reweighting approaches for temporal shifts and sophisticated detection methods for post-market surveillance, provide a multi-layered defense against the pervasive challenge of distribution shift in biomedical AI. As the field progresses, the integration of causal principles with graph-based representations offers the most promising path toward robust, generalizable models that maintain performance across diverse clinical environments and evolving healthcare practices. Future work should focus on standardized benchmarking frameworks, computational efficiency improvements for real-time deployment, and regulatory pathways for clinically validated causal claims.

The integration of multimodal biomedical data is a pivotal challenge in modern healthcare research. Technological advancements now provide a wealth of information from diverse sources, including genomic sequences, transcriptomics, proteomics, medical images, electronic health records, and physiological time-series data [55] [56]. However, this data is often characterized by sparsity, high dimensionality, noise, and heterogeneous formats, making fusion and joint analysis computationally and statistically demanding [55] [56]. The selection of an appropriate data fusion strategy becomes critical for building accurate predictive models for tasks such as disease diagnosis, survival prediction, and drug discovery.

This guide focuses on benchmarking Graph Neural Networks against other machine learning methods for handling biomedical data fusion. GNNs have emerged as particularly powerful tools because they can natively model complex, structured relationships between biological entities—such as protein-protein interactions, molecular structures, and patient-provider networks—that traditional methods often struggle to represent effectively [2] [57] [58]. We objectively compare the performance of various fusion strategies and computational architectures through structured experimental data and detailed methodological protocols.

Comparative Performance of Data Fusion Strategies

Quantitative Benchmarking of Fusion Approaches

Table 1: Performance comparison of multimodal fusion strategies on biomedical tasks

Fusion Strategy	Model Architecture	Application Domain	Performance Metric	Result	Key Advantage
Late Fusion [56]	Ensemble of Gradient Boosting & Random Forest	Cancer Survival Prediction	C-index	Outperformed early fusion	Resists overfitting with high-dimensional features
Intermediate Fusion [55]	Adaptive Multimodal Fusion Network (AMFN)	Biomedical Time Series Prediction	Predictive Accuracy	Superior to unimodal models	Dynamically captures inter-modal dependencies
Early Fusion [56]	Concatenated Feature Inputs	Cancer Survival Prediction	C-index	Underperformed late fusion	Prone to overfitting with low sample size
Graph-based Fusion [58]	HINormer	Medical Claims Fraud Detection	F-score	84% (small dataset)	Captures complex entity relationships
Graph-based Fusion [2]	GCN2	Cancer-Driver Gene Identification	Balanced Accuracy	0.807 ± 0.035	Leverages network topology

Table 2: Performance comparison of GNN architectures on biomedical benchmark tasks

GNN Architecture	Graph Type	Task	Performance	Baseline Comparison	Reference
GCN2	Molecular/PPI Networks	Cancer-driver gene identification	0.807 BACC	Outperformed LR baseline	[2]
HINormer	Heterogeneous Healthcare Claims	Fraud detection	84% F-score (small dataset)	Effective on complex entities	[58]
RE-GraphSAGE	Heterogeneous Healthcare Claims	Fraud detection	83% F-score (small dataset)	Adapts to healthcare data heterogeneity	[58]
XATGRN	Gene Regulatory Networks	Regulatory relationship prediction	Outperforms 22 baseline models	Handles skewed degree distribution	[59]
ErwaNet	Spatial Transcriptomics	Gene expression prediction	State-of-the-art performance	Captures local/global tissue features	[60]

Analysis of Comparative Performance

The experimental data reveals that late fusion strategies consistently outperform early fusion approaches in scenarios with high-dimensional features and limited samples, which is characteristic of many biomedical datasets [56]. This advantage stems from late fusion's resistance to overfitting, as it trains separate models on each modality before combining predictions.

Graph Neural Networks demonstrate particular strength in applications where the inherent relationships between entities are crucial for prediction. In the GNN-Suite benchmark, all evaluated GNN architectures (GAT, GCN, GIN, GraphSAGE, etc.) significantly outperformed a logistic regression baseline, demonstrating the value of network-based learning over feature-only approaches [2]. The GCN2 model achieved the highest balanced accuracy (0.807 ± 0.035) on a STRING-based protein-protein interaction network for identifying cancer-driver genes.

For heterogeneous data involving multiple entity types (patients, providers, diagnoses), specialized GNN architectures like HINormer and RE-GraphSAGE achieved F-scores up to 84% in medical claims fraud detection, showcasing their ability to capture complex relational patterns that traditional methods miss [58].

Experimental Protocols for Data Fusion Strategies

Protocol 1: Late Fusion for Cancer Survival Prediction

The AstraZeneca-AI multimodal pipeline employs a systematic late fusion approach for predicting overall survival in cancer patients [56]:

• Data Preparation: Collect multi-omics data (transcripts, proteins, metabolites) and clinical factors from TCGA. Address missingness through appropriate imputation techniques and apply batch normalization to gene expression data.
• Feature Selection: Perform modality-specific dimensionality reduction using linear (Pearson) or monotonic (Spearman) correlation methods to handle high-dimensional spaces (10³-10⁵ features).
• Model Training: Train separate survival prediction models for each modality using ensemble methods like gradient boosting or random forests, which outperform deep neural networks on tabular biomedical data.
• Fusion and Evaluation: Combine predictions from all modality-specific models using weighted averaging or stacking. Evaluate final model performance using C-index with confidence intervals across multiple train-test splits to ensure statistical robustness.

Protocol 2: Adaptive Multimodal Fusion for Time Series Data

The Adaptive Multimodal Fusion Network (AMFN) addresses biomedical time series challenges through these key steps [55]:

• Data Alignment: Employ attention-based alignment to handle temporal misalignment across physiological signals, imaging, and EHR data.
• Feature Extraction: Use graph-based representation learning to capture inter-modal dependencies while preserving modality-specific characteristics.
• Uncertainty-Aware Fusion: Implement a modality-adaptive fusion mechanism with uncertainty-aware learning to weight modalities based on their noise levels and predictive confidence.
• Validation: Evaluate on real-world biomedical datasets for predictive accuracy, robustness to missing data, and clinical interpretability through attention-based feature attribution.

Protocol 3: GNN Benchmarking with GNN-Suite

The GNN-Suite framework provides standardized benchmarking for GNN architectures in computational biology [2]:

• Graph Construction: Build molecular networks from protein-protein interaction data (STRING, BioGRID) and annotate nodes with genomic features (PCAWG, PID, COSMIC-CGC).
• Model Configuration: Implement diverse GNN architectures (GAT, GCN, GIN, GraphSAGE, etc.) as standardized two-layer models with uniform hyperparameters (dropout=0.2, Adam optimizer with LR=0.01).
• Training Protocol: Use an 80/20 train-test split over 300 epochs with adjusted binary cross-entropy loss to handle class imbalance.
• Evaluation: Execute 10 independent runs with different random seeds and report balanced accuracy as the primary metric for robust statistical comparison.

Visualization of Computational Workflows

Late Fusion Strategy for Survival Prediction

Graph Neural Network Architecture for Biomedical Data

Table 3: Key computational resources for biomedical data fusion research

Resource Name	Type	Primary Function	Application Example	Reference
GNN-Suite	Benchmarking Framework	Standardized GNN evaluation	Comparing GNN architectures on PPI networks	[2]
PyTorch Geometric	Deep Learning Library	GNN implementation and training	Building bioreaction-variation networks	[57]
AZ-AI Multimodal Pipeline	Data Fusion Pipeline	Multimodal feature integration	Late fusion for cancer survival prediction	[56]
TCGA (The Cancer Genome Atlas)	Data Repository	Multi-omics cancer patient data	Training survival prediction models	[56]
IGB-H Dataset	Graph Dataset	Large-scale heterogeneous graph	Benchmarking RGAT performance	[18]
STRING/BioGRID	Biological Database	Protein-protein interaction data	Constructing molecular networks for GNNs	[2]
BioBERT	NLP Model	Biomedical text processing	Encoding experimental context from literature	[57]
LukePi	Pre-training Framework	Self-supervised graph pre-training	Predicting biomedical interactions with limited labels	[48]

The integration of multimodal biomedical data requires sophisticated strategies that can handle heterogeneity, sparsity, and noise while extracting complementary information across modalities. Our comparative analysis demonstrates that late fusion strategies often outperform early fusion in scenarios with high-dimensional data and limited samples, as they reduce overfitting risks by training separate models on each modality before combining predictions [56].

Graph Neural Networks emerge as particularly powerful tools for biomedical data fusion, consistently outperforming traditional machine learning baselines in applications that benefit from modeling structured relationships [2] [58]. Specialized GNN architectures like HINormer for heterogeneous graphs [58], XATGRN for gene regulatory networks with skewed degree distributions [59], and ErwaNet for spatial transcriptomics [60] demonstrate the versatility of graph-based approaches across diverse biomedical domains.

The choice of an optimal fusion strategy depends critically on dataset characteristics—including sample size, dimensionality, modality heterogeneity, and missing data patterns—as well as the specific predictive task. Researchers should consider these factors when selecting between late fusion, intermediate fusion, or graph-based approaches for their multimodal biomedical data challenges.

Computational Complexity and Scalability Challenges for Large-Scale Biomedical Networks

The application of Graph Neural Networks (GNNs) to biomedical data represents a paradigm shift from traditional machine learning, moving beyond isolated data points to models that capture the complex, interconnected nature of biological systems. Biomedical networks—spanning molecular interactions, protein-protein interfaces, disease comorbidity patterns, and patient similarity graphs—provide powerful frameworks for modeling biological complexity. However, as the scale of these networks expands to encompass billions of relationships across millions of biological entities, computational complexity and scalability emerge as critical bottlenecks. The sheer volume of biomedical data, exemplified by knowledge graphs like PrimeKG containing over 4 million relationships connecting 17,000 diseases, demands specialized approaches that traditional GNN architectures cannot efficiently handle [7].

The scalability challenge is twofold, involving both structural and computational dimensions. Structurally, GNNs rely on iterative message-passing where nodes aggregate information from neighbors, a process that becomes computationally prohibitive as graph size increases due to the exponential growth of neighbor nodes with each additional layer. Computationally, memory consumption and inference times escalate dramatically when applying traditional GNN architectures to large-scale biomedical graphs, creating barriers to real-time clinical applications and even batch research processing [61]. This review systematically benchmarks GNN performance against traditional machine learning methods, examines innovative architectural responses to scalability constraints, and provides experimental frameworks for evaluating computational efficiency in biomedical contexts.

Performance Benchmarking: GNNs vs. Traditional Methods

Quantitative Performance Comparisons

Graph Neural Networks demonstrate a consistent performance advantage over traditional machine learning methods by explicitly modeling relational information, though this advantage comes with increased computational overhead. The following table synthesizes performance metrics across multiple biomedical applications:

Table 1: Performance comparison between GNNs and traditional ML methods on biomedical tasks

Application Domain	Task	Best Performing GNN (Accuracy/Metric)	Traditional ML Method (Accuracy/Metric)	Performance Delta
Cancer Gene Identification	Driver Gene Prediction	GCN2 (Balanced Accuracy: 0.807)	Logistic Regression (Balanced Accuracy: Not specified)	All GNNs outperformed LR baseline [2]
Sepsis Classification from Blood Count Data	Medical Diagnosis	GAT on Patient-Centric Graphs (AUROC: 0.9565)	XGBoost (AUROC: Comparable to similarity-GNNs ~0.87)	~9% AUROC improvement for temporal graphs [16]
Sepsis Classification (Similarity Graphs)	Medical Diagnosis	Standard GNNs (AUROC: 0.8747)	XGBoost/Neural Networks (AUROC: Comparable)	Comparable performance [16]
Drug-Disease Association (DDA) Prediction	Biomedical Knowledge Graph Completion	PT-KGNN with Large-scale KG Pre-training	Traditional Feature-based Methods	Superior performance using semantic/structural embeddings [7]
Drug-Drug Interaction (DDI) Prediction	Biomedical Knowledge Graph Completion	PT-KGNN with Large-scale KG Pre-training	Traditional Feature-based Methods	Superior performance using semantic/structural embeddings [7]

The performance advantage of GNNs is particularly pronounced in scenarios where relational structure provides critical signals not captured by node features alone. In cancer driver gene identification, all evaluated GNN architectures (including GAT, GCN, GraphSAGE, GIN, and others) consistently outperformed logistic regression baselines, demonstrating that network-based learning provides substantial advantages over feature-only approaches [2]. Similarly, for temporal medical data, GNNs configured to leverage time-series information through patient-centric graphs achieved remarkable 9% AUROC improvements in sepsis classification compared to both traditional methods and GNNs operating on simple similarity graphs [16].

The Pre-training Advantage in Biomedical Knowledge Graphs

The scale of pre-training knowledge graphs directly correlates with downstream task performance in biomedical applications. The PT-KGNN framework demonstrates that pre-training on large-scale biomedical knowledge graphs significantly enhances performance for drug-drug interaction (DDI) and drug-disease association (DDA) prediction on independent datasets [7]. This framework employs self-supervised learning strategies using GNNs to learn node embeddings that capture both semantic and structural information from biomedical KGs, incorporating diverse biological entities beyond simply drugs and diseases. Importantly, embeddings derived from larger biomedical KGs demonstrate superior performance compared to those from smaller KGs, establishing a clear scaling law relationship between pre-training graph size and predictive accuracy [7].

Computational Complexity Analysis

Fundamental Scalability Challenges

GNNs face two fundamental challenges when applied to large-scale biomedical networks: over-smoothing and computational intractability. Over-smoothing occurs when excessive message passing causes node representations to become indistinguishable, particularly problematic in deep networks incorporating high-order neighbors [61]. This phenomenon is especially prevalent in biomedical networks where meaningful signals may require aggregation from distant nodes, yet increasing network depth diminishes discriminative power.

Computational intractability stems from the exponential neighbor expansion in large-scale graphs. Traditional GNN architectures suffer from high model complexity and increased inference time due to redundant information aggregation across exponentially growing neighbor sets [61]. As each additional layer incorporates neighbors at increasing distances, the computational and memory requirements grow combinatorially, creating practical deployment barriers for massive biomedical graphs like Bioteque, which contains over 450,000 biological entities and 30 million relationships [7].

Architectural Innovations for Scalability

Several innovative architectures have emerged specifically to address the computational complexity challenges in large-scale biomedical networks:

• ScaleGNN: This framework simultaneously addresses over-smoothing and scalability through adaptive high-order feature fusion. It employs a trainable mechanism to construct and refine multi-hop neighbor matrices, allowing the model to selectively emphasize informative high-order neighbors while reducing unnecessary computational costs [61]. A key innovation is the Local Contribution Score (LCS), which enables retention of only the most relevant neighbors at each order, preventing redundant information propagation.

• Pre-computation Methods: Approaches like SIGN, S2GC, and NARS decouple feature propagation from non-linear transformation, enabling feature propagation without model parameter training [61]. These methods pre-compute propagated features, dramatically reducing computational overhead during training while maintaining performance.

• Hybrid Graph + Vector Search: TigerGraph's implementation combines graph search for multi-hop relational patterns with vector similarity matching, optimizing both structural awareness and semantic similarity [62]. This hybrid approach enables efficient anomaly detection and pattern recognition at scale.

Table 2: Computational efficiency of scalable GNN architectures

Architecture	Key Innovation	Computational Advantage	Biomedical Applicability
ScaleGNN	Adaptive high-order feature fusion with Local Contribution Score	Reduces redundant computation by filtering irrelevant high-order neighbors	Large-scale heterogeneous biomedical knowledge graphs [61]
Pre-computation Methods (SGC, SIGN, S2GC)	Decoupling feature propagation from transformation	Eliminates iterative message passing during training	Molecular property prediction on large compound libraries [61]
GraphSAGE	Neighbor sampling for mini-batch training	Enables training on massive graphs that don't fit in memory	Patient similarity networks with millions of nodes [62]
SeHGNN	Relation-wise separate neighbor aggregation	Reduces information loss while maintaining efficiency	Heterogeneous biomedical data with multiple entity and relationship types [61]

Experimental Protocols and Benchmarking Methodologies

Standardized Evaluation Frameworks

Robust evaluation of computational complexity and scalability requires standardized benchmarking frameworks. GNN-Suite provides a modular framework for constructing and benchmarking GNN architectures in computational biology, standardizing experimentation and reproducibility using the Nextflow workflow management system [2]. This framework enables fair comparisons among diverse GNN architectures through standardized configurations:

• All GNNs are configured as two-layer models with uniform hyperparameters (dropout = 0.2; Adam optimizer with learning rate = 0.01)
• Models are evaluated over 10 independent runs with different random seeds to yield statistically robust performance metrics
• Balanced accuracy (BACC) serves as the primary metric for class-imbalanced biomedical datasets
• An 80/20 train-test split with 300 training epochs provides consistent evaluation conditions

For biomedical knowledge graph applications, the PT-KGNN framework employs a consistent evaluation protocol where pre-training occurs on biomedical KGs in a self-supervised strategy using GNNs, followed by downstream task fine-tuning [7]. Node embeddings preserving abundant information from the biomedical KG are extracted, and concatenation of node pairs' embeddings serves as input to a multi-layer perceptron (MLP) predictor that predicts relation scores of node pairs.

Temporal Graph Construction for Clinical Data

For clinical time series data, specialized graph construction methodologies enable effective temporal modeling while managing complexity. In sepsis classification from complete blood count data, two graph construction approaches demonstrate different computational profiles:

• Similarity Graphs: Homogeneous k-nearest neighbors (k-nn) graphs connect blood count measurements directly based on normalized Euclidean distance of features [16]. Heterogeneous similarity graphs indirectly connect patient samples through discretized blood parameter nodes, reducing sensitivity to outliers.

• Patient-Centric Graphs: These incorporate time-series information by connecting consecutive blood count samples from the same patient based on measurement times [16]. This approach achieves superior performance (AUROC: 0.9565) but requires careful management of temporal dependencies.

Diagram Title: Experimental workflow for biomedical GNNs

Table 3: Essential tools and frameworks for biomedical GNN research

Tool/Resource	Type	Function	Application Context
GNN-Suite [2]	Benchmarking Framework	Standardizes GNN experimentation and reproducibility	Comparing GNN architectures on biological networks
BioKG [7]	Knowledge Graph	Integrated biomedical KG with 6 node types, 12 edge types	Pre-training GNNs for drug discovery applications
PT-KGNN [7]	Pre-training Framework	Learns node embeddings capturing semantic and structural information	Transfer learning for downstream prediction tasks
TigerGraph [62]	Graph Database	Native graph storage enabling real-time traversal of billion-edge graphs	Large-scale biomedical network analysis
ScaleGNN [61]	Scalable GNN Architecture	Adaptive high-order feature fusion for large graphs	Biomedical networks with complex relational patterns
cBioPortal [63]	Data Repository	Cancer genomics and clinical data with publication linkages	Real-world biomedical hypothesis validation
DGL [7]	Software Library	Graph neural network framework with PyTorch backend	Implementing and training custom GNN architectures

The computational complexity and scalability challenges facing large-scale biomedical networks represent both a formidable barrier and a catalyst for innovation in graph neural network architectures. Our benchmarking analysis reveals that while GNNs consistently outperform traditional machine learning methods on relational biomedical data, this performance advantage comes with significant computational costs that must be strategically managed. The emergence of specialized frameworks like ScaleGNN for adaptive feature fusion and PT-KGNN for knowledge graph pre-training demonstrates the field's evolving response to these challenges.

Successful navigation of the scalability trade-off requires purposeful architectural selection aligned with specific biomedical application requirements. For temporal clinical data like sepsis prediction, patient-centric GNN configurations deliver exceptional performance gains worth their computational overhead. For large-scale knowledge graph completion, pre-training and transfer learning strategies maximize predictive accuracy while amortizing computational costs across multiple downstream tasks. As biomedical networks continue to grow in scale and complexity, the development of increasingly sophisticated scalability solutions will play a pivotal role in enabling the next generation of biomedical AI applications.

Healthcare artificial intelligence (AI) systems routinely fail when deployed across institutions, with documented performance drops and the perpetuation of discriminatory patterns embedded in historical data [28]. This brittleness stems from a fundamental mismatch between what standard machine learning optimizes—statistical associations—and what clinical decision-making requires—understanding of causal mechanisms [28]. The COVID-19 pandemic exposed these limitations with devastating clarity, where predictive models trained on historical data failed catastrophically when confronted with a novel pathogen and rapidly evolving clinical practices [28]. In one stark example, a widely deployed risk prediction algorithm systematically underestimated disease severity for Black patients by relying on healthcare costs as a proxy for health needs, despite Black patients receiving less aggressive treatment even when experiencing an equivalent disease burden [28].

The distinction between correlation and causation maps directly to Pearl's Causal Hierarchy, which organizes reasoning into three levels of increasing inferential power [28] [64]. Level 1 (Association) addresses "what is?" questions through conditional probabilities P(Y|X)—the domain where standard machine learning excels. Level 2 (Intervention) concerns "what if we do?" questions, formalized using the do-operator P(Y|do(X)), which is essential for treatment planning. Level 3 (Counterfactual) addresses "what would have been?" questions critical for personalized medicine and retrospective analysis [28] [64]. Biomedical systems inherently form networks across multiple biological scales, making graph representations a natural framework for encoding biological relationships, from molecular interactions and brain connectivity to disease comorbidity patterns [28] [65]. Causal Graph Neural Networks (Causal GNNs) emerge at the intersection of these concepts, combining graph-structured representations with causal inference principles to learn invariant biological mechanisms rather than spurious correlations [28] [64].

Performance Benchmarking: Causal GNNs vs. Alternative Approaches

Quantitative comparisons across diverse biomedical applications consistently demonstrate that causality-informed GNNs achieve superior generalizability and robustness compared to both traditional machine learning and non-causal GNN baselines.

Table 1: Performance Comparison Across Biomedical Domains

Application Domain	Model Architecture	Key Performance Metrics	Performance Summary
Axillary Lymph Node Metastasis Prediction [52]	Graph Convolutional Network (GCN)	AUC: 0.77 (95% CI: 0.69-0.84)	Outperformed GAT, GIN, and traditional ML
Cuffless Blood Pressure Estimation [66]	CiGNN (Causality-informed GNN)	MAD SBP: 3.77 mmHg; MAD DBP: 2.52 mmHg	Surpassed knowledge-driven and data-driven models
CAD Mortality Prediction [67]	Lightweight GCN with causal features	Recall: 93.02%; NPV: 89.42%	Higher recall than NN, LR, SVM, and RF
Biomarker Discovery [68]	Causal-GNN with multi-layer graphs	Consistently high predictive accuracy across 4 datasets	Identified more stable biomarkers vs. traditional methods
Drug Repositioning [69]	DREAM-GNN (Dual-Route GNN)	Superior in recovering artificially removed candidates	Outperformed DRRS, BNNR, PREDICT, deepDR

Diagnostic and Prognostic Applications

In breast cancer diagnosis, GNNs applied to axillary ultrasound and histopathologic data demonstrated strong performance in predicting axillary lymph node metastasis (ALNM), a critical factor in surgical decision-making [52]. The Graph Convolutional Network (GCN) model achieved an AUC of 0.77, outperforming both Graph Attention Networks (GAT) and Graph Isomorphism Networks (GIN) on this clinical task [52]. This performance highlights the potential of GNNs to provide a non-invasive tool for detecting ALNM, potentially reducing the need for invasive surgical procedures like sentinel lymph node biopsy [52].

For cardiovascular prognosis, a causality-aware lightweight GCN model predicted long-term mortality in coronary artery disease patients with remarkable recall of 93.02% and negative predictive value of 89.42% [67]. This approach utilized a hybrid feature selection method combining logistic regression with propensity score matching to identify potentially causal features, then constructed a graph connecting patients with similar causal characteristics [67]. The model's "lightweight" nature—utilizing only a concise set of critical features—enhances its potential for real-time clinical implementation while maintaining high predictive performance [67].

Therapeutic and Monitoring Applications

In therapeutic development, Causal GNNs have demonstrated particular value in biomarker discovery and drug repositioning. The Causal-GNN framework for biomarker discovery integrates causal inference with multi-layer graph neural networks to identify stable biomarkers from high-throughput transcriptomic data, achieving consistently high predictive accuracy across four distinct datasets and four independent classifiers [68]. Unlike traditional methods that often conflate spurious correlations with genuine causal effects, this approach incorporates causal effect estimation coupled with a GNN-based propensity scoring mechanism that leverages cross-gene regulatory networks [68].

For continuous physiological monitoring, the CiGNN framework for cuffless blood pressure estimation seamlessly integrates causality with graph neural networks, achieving mean absolute differences of 3.77 mmHg for systolic BP and 2.52 mmHg for diastolic BP [66]. This approach employs a two-stage methodology: first generating a causal graph between BP and wearable features through causal inference algorithms, then utilizing a spatio-temporal GNN to learn from this causal graph for refined BP estimation [66]. The method demonstrated superior performance across diverse populations, including subjects of different age groups, with and without hypertension, and during various maneuvers that induce BP changes [66].

Experimental Protocols and Methodological Frameworks

Causal Graph Construction and Feature Selection

A common methodological theme across Causal GNN applications is the rigorous approach to causal graph construction and feature selection. In the CAD mortality prediction study, researchers employed a hybrid logistic regression-propensity score matching (LR-PSM) approach to identify causal features [67]. This method first uses logistic regression to identify features with significant associations with the outcome, then applies propensity score matching to select features with potentially causal relationships, finally validating these selections through domain knowledge [67]. The resulting causal features, alongside demographic variables, were used to create a patient similarity graph, drawing edges between patients with similar causal features [67].

For cuffless blood pressure monitoring, the CiGNN framework employs a more complex two-stage causal discovery process [66]. The initial causal graph is identified with the Fast Causal Inference (FCI) algorithm, which can detect causal relationships in the presence of unmeasured confounders but often leaves some edge directions unoriented [66]. Subsequently, Causal Generative Neural Networks (CGNN) algorithm orients and modifies the initial graph, producing a directed causal graph that serves as prior knowledge for the subsequent spatio-temporal GNN [66]. This approach addresses the limitation of Markov equivalence classes that plagues many constraint-based causal discovery algorithms [66].

Model Architectures and Training Paradigms

Causal GNN architectures incorporate causal principles through various innovative mechanisms. The Causality-inspired Graph Neural Network (CI-GNN) uses Granger causality-inspired conditional mutual information to quantify causal strength for graph edges, identifying influential subgraphs representing genuine causal connections rather than spurious correlations [64]. The Debiasing via Disentangled Causal Substructure (DisC) framework employs a dual-encoder GNN architecture and contrastive learning to separate causal features (which remain invariant across environments) from spurious features (which vary across environments) [64].

For interventional prediction without experimental data, CaT-GNN (Causal Temporal Graph Neural Network) implements interventional reasoning through architectural modifications encoding backdoor adjustment, applying mixup augmentation specifically to environmental confounders [64]. RC-Explainer (Reinforced Causal Explainer) leverages reinforcement learning to discover optimal graph interventions that maximize causal effects while accounting for confounding [64]. These methodologies enable Causal GNNs to answer "what if?" questions essential for treatment planning without requiring costly and potentially unethical randomized trials.

Validation Approaches for Causal Claims

Validating causal claims requires going beyond traditional predictive metrics. Researchers have proposed multi-modal evidence triangulation frameworks that combine biological plausibility, replication across independent cohorts, natural experiments, prospective intervention studies, and sensitivity analyses [28] [64]. Tiered evidentiary standards help distinguish causally-inspired architectures (which use causal terminology but lack rigorous validation) from causally-validated discoveries (which provide strong evidence for causal mechanisms) [28] [64]. For example, in biomarker discovery, stability across multiple independent datasets and biological interpretability through existing knowledge of gene regulatory networks serve as important validation criteria [68].

Signaling Pathways and Biological Mechanisms

Causal GNNs excel at elucidating complex biological mechanisms by modeling how signals propagate through biomolecular networks. In cancer research, causality-aware GNNs have been applied to the human DNA damage and repair pathway, specifically focusing on the TP53 regulon in a pan-cancer study across cell lines and tumor samples [65]. This approach combines mathematical programming optimization with GNNs to reconstruct gene regulatory networks from genomic and transcriptomic data, then classifies these networks based on TP53 mutation types [65].

The framework employs Prior Knowledge Networks (PKNs) from established databases to reconstruct gene networks, then tailors GNNs to classify each network as a single data point at the graph level [65]. This enables the identification of mutations with distinguishable functional profiles that can be related to specific phenotypes, providing a data-driven pipeline for genotype-to-phenotype translation [65]. The GNN classifier incorporates multiple biologically meaningful features, including node activities, edge attributes representing modes of regulation (activation/inhibition), and community structures within the reconstructed networks [65].

Implementing and validating Causal GNNs requires specialized computational resources, biological databases, and methodological frameworks. The table below catalogs key "research reagent solutions" essential for working with Causal GNNs in biomedical research.

Resource Category	Specific Tools & Databases	Primary Function	Application Examples
Causal Discovery Algorithms	Fast Causal Inference (FCI), Causal Generative Neural Networks (CGNN)	Identify causal graphs from observational data	Orienting edges in blood pressure monitoring [66]
Biological Knowledge Bases	Prior Knowledge Networks (PKNs), Protein-Protein Interaction databases	Provide structured biological prior knowledge	TP53 regulon analysis in cancer [65]
Biomedical Language Models	ChemBERTa, ESM-2, BioBERT	Generate semantic embeddings for drugs and diseases	Drug repositioning with DREAM-GNN [69]
Graph Neural Network Frameworks	PyTorch Geometric, Deep Graph Library	Implement GNN architectures and message passing	All cited applications [52] [66] [67]
Causal Validation Frameworks	Sensitivity analysis (E-values), Multi-modal triangulation	Validate causal claims beyond predictive accuracy	Tiered evidentiary standards [28] [64]

The convergence of causal inference with graph neural networks establishes a foundation for what researchers term Causal Digital Twins—dynamic computational models built on causal GNN frameworks that integrate multi-omics data, longitudinal imaging, clinical history, and knowledge graphs [28] [64]. These digital twins would enable clinicians to perform in silico experiments by simulating therapeutic interventions via the do-operator, predicting patient-specific outcomes across molecular, cellular, and phenotypic levels before administering actual treatments [28] [64].

A powerful synergy is emerging between Large Language Models (LLMs) and Causal GNNs, where LLMs excel at hypothesis generation from unstructured clinical data, while Causal GNNs provide mechanistic validation and quantification of these hypotheses using structured biomedical data [28] [64]. Despite substantial progress, significant challenges remain in computational complexity, validation standards, and clinical integration [28]. However, the demonstrated successes across diagnostic, prognostic, therapeutic, and monitoring applications provide compelling evidence that Causal GNNs represent a transformative approach for moving beyond spurious correlations to invariant biological mechanisms in biomedical AI.

The Proof is in the Performance: Validating and Comparing GNNs Against Traditional ML Benchmarks

This guide provides an objective performance comparison of GNN-Suite, a novel benchmarking framework for Graph Neural Networks (GNNs) in biomedical informatics. The analysis demonstrates that GNN-Suite enables standardized evaluation of multiple GNN architectures, revealing that GCN2 achieved the highest balanced accuracy (0.807 ± 0.035) in cancer-driver gene identification tasks. All tested GNN models significantly outperformed traditional logistic regression baselines, underscoring the critical value of incorporating network structure into biomedical data analysis.

---

GNN-Suite represents the first Nextflow-based benchmarking framework specifically designed for evaluating GNN architectures in biomedical informatics [15] [70]. Built with the scientific workflow system Nextflow, the framework provides a modular, reproducible pipeline for comparing diverse GNN architectures on biologically relevant tasks [2] [71]. Its design follows FAIR principles (Findable, Accessible, Interoperable, Reusable) to ensure adaptability for future research, allowing researchers to systematically evaluate model performance while maintaining consistent training and evaluation procedures [15].

The framework supports nine GNN architectures: GAT, GAT3H, GCN, GCN2, GIN, GTN, HGCN, PHGCN, and GraphSAGE [2] [15]. These models are implemented using the PyTorch Geometric (PyG) library and can be benchmarked against traditional machine learning baselines like logistic regression [15]. To demonstrate its utility, the developers applied GNN-Suite to the critical biological problem of identifying cancer-driver genes using protein-protein interaction networks [71].

Experimental Workflow

The following diagram illustrates the standardized benchmarking process implemented in GNN-Suite:

Experimental Methodology

Research Reagent Solutions

Component	Function	Data Sources
Network Data	Provides graph structure (nodes/edges)	STRING, BioGRID PPI databases [2] [15]
Node Features	Annotates nodes with biological features	PCAWG, PID, COSMIC-CGC repositories [2] [15]
GNN Architectures	Implements various graph learning approaches	GAT, GCN, GraphSAGE, GIN, GTN, HGCN, PHGCN, GCN2 [2]
Evaluation Metrics	Quantifies model performance	Balanced Accuracy (BACC), Precision, Recall, AUC [15]

Data Preparation and Network Construction

The benchmark utilized protein-protein interaction (PPI) data from STRING and BioGRID databases to construct molecular networks where nodes represented proteins and edges represented observed interactions [15]. Nodes were annotated with cancer gene association likelihoods derived from Pan-Cancer Analysis of Whole Genomes (PCAWG) data, while known cancer drivers were labeled using gene lists from Pathway Indicated Drivers (PID) and COSMIC Cancer Gene Census (COSMIC-CGC) repositories [2] [15]. This setup created a realistic biological context for evaluating GNN performance on node classification tasks.

Standardized Training Protocol

All GNN architectures were configured as standardized two-layer models and trained using uniform hyperparameters to ensure fair comparisons [2] [15]. The training protocol employed:

• Dropout rate: 0.2
• Optimizer: Adam with learning rate = 0.01
• Loss function: Adjusted binary cross-entropy to address class imbalance
• Training duration: 300 epochs
• Train-test split: 80/20 ratio
• Evaluation: 10 independent runs with different random seeds

This consistent approach eliminated performance variations due to implementation differences rather than architectural capabilities [2].

Performance Comparison Results

GNN Architecture Performance on Cancer-Driver Gene Identification

The following table summarizes the quantitative performance of GNN architectures benchmarked using GNN-Suite on STRING-based networks:

Model Type	Balanced Accuracy (BACC)	Key Findings
GCN2	0.807 ± 0.035	Highest performing architecture [2]
All GNN Models	Significantly outperformed LR baseline	Demonstrated value of network-based learning [2]
Logistic Regression (Baseline)	Lower than all GNNs	Feature-only approach limitations [2]

The comprehensive benchmarking revealed that while GCN2 achieved the highest performance, all GNN architectures demonstrated significant improvements over the logistic regression baseline, highlighting the critical advantage of incorporating network structure into biological data analysis [2]. The similar performance across many architectures suggests that benchmarked GNNs effectively captured the network structure of the data, with performance differences being relatively modest between architectures [71].

Comparative Analysis with Traditional Methods

GNN-Suite enables direct comparison between GNNs and traditional machine learning approaches, revealing several key advantages of graph-based methods:

• Structural Learning Capability: Unlike traditional ML that treats data points independently, GNNs learn from the structure of the graph itself, allowing them to capture complex biological relationships that feature-only approaches miss [62].

• Contextual Prediction: GNNs update node representations based on neighbor features, enabling more accurate predictions in biological contexts where entities are inherently interconnected [62].

• Multi-Hop Relationship Analysis: GNNs can natively handle complex multi-hop relationships in biological networks, which traditional SQL and NoSQL databases struggle to process efficiently [62].

Implementation and Accessibility

Technical Architecture

The GNN-Suite pipeline is implemented in Nextflow (v22.10.1) to ensure modularity and reproducibility [15]. The main workflow script defines processes for training GNNs, plotting metrics, and computing evaluation statistics, while experiment-specific configurations control data files, epochs, replicas, and model architectures [15]. The framework provides a Docker image via GitHub Container Registry to simplify setup and create consistent environments for PyTorch, PyTorch Geometric, and CUDA dependencies [15].

Evaluation Metrics and Reproducibility

GNN-Suite captures comprehensive metrics to facilitate thorough model comparison:

• Primary metric: Balanced Accuracy (BACC) - selected due to class imbalance in biological data [15]
• Additional metrics: Loss, true negatives (TN), false positives (FP), false negatives (FN), true positives (TP), precision, recall, accuracy, and AUC [15]

The framework's design emphasizes reproducibility, with all configuration files, model definitions, and evaluation scripts publicly available through a dedicated GitHub repository to enable other researchers to perform similar investigations in computational biology [71].

Discussion and Future Directions

The development of GNN-Suite addresses a critical need in computational biology for standardized comparison of GNN architectures, which have traditionally been implemented with different training and evaluation procedures that complicate direct performance comparisons [15]. By providing a unified framework, GNN-Suite enables more robust assessment of architectural innovations in graph learning for biomedical applications.

Future work will explore additional omics datasets and further refine network architectures to enhance predictive accuracy and interpretability in complex biomedical applications [2]. The framework's modular design also allows for the integration of new GNN architectures as they emerge, ensuring its continued relevance as the field evolves.

For biomedical researchers, GNN-Suite offers a valuable tool for unlocking complex insights from biological networks, potentially accelerating discoveries in areas such as drug target identification, molecular interaction analysis, and personalized medicine approaches [70].

The adoption of artificial intelligence in biomedical research introduces a critical question for practitioners: which model architecture most effectively unlocks insights from complex clinical data? While traditional machine learning methods like Logistic Regression (LR) and XGBoost offer strong performance on structured data, and Convolutional Neural Networks (CNNs) excel in image analysis, Graph Neural Networks (GNNs) present a paradigm shift for inherently relational data. This guide provides an objective, evidence-based comparison of these competing methodologies through quantitative results from recent clinical case studies. We focus on direct performance comparisons across key biomedical tasks—including gene expression inference, computational histopathology, and drug discovery—to equip researchers, scientists, and drug development professionals with the empirical data needed to inform model selection. The comparative analysis is framed within the broader thesis that GNNs are not a one-size-fits-all solution but offer distinct advantages for tasks where the relational or topological structure of the data is central to the biological question.

The following table synthesizes key findings from recent head-to-head comparisons between GNNs and other machine learning models on specific clinical and biomedical tasks.

Table 1: Summary of Head-to-Head Model Performance on Clinical Tasks

Clinical Task	Best Performing Model	Key Comparative Metric(s)	Runner-Up Model(s)	Performance Gap & Context
Gene Expression Inference [72]	Graph Neural Network (GNN)	Sum of Squared Errors (SE): ~20% lower than LRSpearman's Correlation (SCC): HigherData Efficiency: Matched LR performance with ~10% of the input features	Linear Regression (LR), k-NN, MLP, Swin Transformer	The GNN significantly outperformed all non-GNN models in inferring RNA-seq values from L1000 landmark transcript data, demonstrating superior accuracy and efficiency.
Cancer Histopathology (e.g., Tumor Classification, Prognosis) [73]	Graph Neural Network (GNN)	Accuracy & Generalization: Superior performance in tumor classification and prognosis prediction by modeling tissue microenvironments as graphs.	Convolutional Neural Network (CNN)	GNNs addressed key CNN limitations, such as loss of contextual information between image patches, leading to better model generalization.
Molecular Property Prediction [74]	Stable Graph Neural Network (S-GNN)	Out-of-Distribution (OOD) Generalization: Surpassed other GNN models on OGB and TUdatasets by reducing prediction bias in unseen test distributions.	Standard GNNs (GCN, GAT)	By de-correlating spurious features, the S-GNN variant demonstrated more stable and robust predictions than standard GNNs under distribution shift.
General Clinical Prediction (Cardiovascular, Cancer) [75] [76]	Random Forest / XGBoost	AUC & Accuracy: RF achieved AUC of 0.85 for cardiovascular disease; SVM achieved 83% accuracy for cancer prognosis.	Support Vector Machines (SVM), Logistic Regression (LR)	In broad analyses of ML for oncology and real-world data, tree-based ensembles like RF and XGBoost were frequently among the top performers for standard structured data.

Key takeaways for practitioners

• Use GNNs for Relational and Structural Data: The primary strength of GNNs is modeling complex relational structures, such as gene interactions, tissue architecture in histopathology slides, and molecular graphs [72] [73]. If a task can be framed as a graph problem, GNNs are likely the superior choice.
• Leverage XGBoost/RF for Tabular Clinical Data: For traditional structured tabular data (e.g., features from electronic health records), tree-based models like Random Forest and XGBoost remain highly competitive and often achieve state-of-the-art results with less computational complexity [75] [76].
• Prioritize GNNs for Data Efficiency: In scenarios where input data is limited or costly to acquire, GNNs can potentially achieve high performance with significantly less input information, as demonstrated in the gene expression inference task [72].
• Consider GNN Variants for Robustness: For real-world deployment where data distribution shifts are a concern, newer GNN architectures focusing on stable learning or causal mechanisms show promise for improved OOD generalization and reliability compared to standard models [74] [28].

Detailed case studies & experimental protocols

Case study 1: Gene expression inference

This study provides a direct, quantitative comparison of a GNN against several non-GNN models for the task of inferring a full transcriptome from a limited set of landmark genes, a common cost-saving technique in genomics [72].

Table 2: Model Performance on Gene Expression Inference Task

Model	Overall Error (↓)	Spearman Correlation (↑)	Pearson Correlation (↑)
GNN (Proposed)	Lowest	Highest	Highest
Linear Regression (LR)	Highest	Lowest	Lowest
k-Nearest Neighbors (k-NN)	Moderate	Moderate	Moderate
Multilayer Perceptron (MLP)	Moderate	Moderate	Moderate
Swin Transformer	Moderate	Moderate	Moderate

Experimental protocol

• Objective: To predict RNA-seq expression values of 12,320 transcripts using L1000 expression values of only 970 landmark transcripts as input [72].
• Dataset: 3,176 tissue samples with paired L1000 and RNA-seq data from GEO (GSE92743). The data was split into training (2,500 samples), validation (500), and test (176) sets [72].
• Model Training & Evaluation:
  • All models were trained on the same paired data to learn the mapping from L1000 to RNA-seq values.
  • Performance was evaluated using Sum of Squared Errors (SE), Spearman's Correlation Coefficients (SCC), and Pearson Correlation Coefficient (PCC) between the inferred and ground-truth RNA-seq values, averaged across all test datasets.
  • A key efficiency metric, Gene-level Recall, was used to determine how many genes were "well-inferred" by each model.

The GNN model's architecture, which represented genes as nodes in a graph, allowed it to effectively capture nonlinear correlations between genes. A critical finding was that the GNN required approximately 10-fold less input information to achieve a level of performance comparable to the Linear Regression model using the full set of input features [72].

Case study 2: Computational histopathology

In computational histopathology, Whole Slide Images (WSIs) are gigapixel-sized digital scans of tissue sections. The prevailing approach using CNNs involves dividing the WSI into small patches, which often leads to a loss of critical contextual information about the tissue microstructure [73].

Experimental protocol & GNN advantage

• Graph Construction: Instead of using patches independently, GNN-based methods construct a graph from the WSI. Nodes represent tissue regions or individual cells. Edges are drawn based on spatial proximity or functional relationships, explicitly modeling the tissue structure as a network [73].
• Model Comparison: In studies comparing GNNs to CNNs on tasks like tumor classification and prognosis prediction, GNNs consistently demonstrated superior performance. The key differentiator was the GNN's ability to aggregate information from neighboring nodes, effectively capturing the complex cellular interactions and tissue organization that are hallmarks of disease pathology [73].
• Clinical Impact: This capability allows GNNs to outperform CNNs in predicting patient outcomes (prognosis) by learning from the spatial arrangement of cells and tissues, which is a strong indicator of cancer aggression and behavior [73].

This section details the essential computational tools, datasets, and model architectures referenced in the featured case studies, providing a foundation for replicating or building upon this research.

Table 3: Key Research Reagents and Resources for GNN Benchmarking

Resource Name	Type	Primary Function / Utility	Relevant Use-Case
IGB-H Dataset [18]	Dataset	A massive heterogeneous graph (547M nodes, 5.8B edges) for large-scale GNN benchmarking.	Node classification (e.g., academic paper topics).
TUDataset [74]	Dataset	A collection of over 120 graph datasets from various domains (chemistry, bioinformatics).	Molecular property prediction, social network analysis.
OGB Datasets [74]	Dataset	A collection of large-scale, diverse benchmark datasets for GNNs.	Robust evaluation of GNNs on molecular graphs and knowledge graphs.
LINCS L1000 & RNA-seq Data [72]	Dataset	Paired gene expression profiles (limited landmarks vs. full transcriptome).	Training and evaluation of gene expression inference models.
RGAT (Relational GAT) [18]	Model	A GNN variant that handles multi-relational graphs (different edge types).	Knowledge graph reasoning, complex heterogeneous data.
Stable-GNN (S-GNN) [74]	Model	A GNN architecture designed for stable learning under distribution shift.	Improving model generalization for real-world clinical deployment.
Causal GNNs [28]	Framework	Integrates causal inference with GNNs to move beyond spurious correlations.	Identifying genuine therapeutic targets, robust treatment prediction.

The head-to-head comparisons presented in this guide reveal a nuanced landscape. No single model class universally dominates all clinical tasks. XGBoost and Random Forest maintain their status as powerful, reliable tools for structured clinical data. However, Graph Neural Networks have established a definitive advantage in scenarios where the underlying data is relational, structural, or network-based. The empirical evidence from gene expression inference and computational histopathology demonstrates that GNNs can achieve higher accuracy and data efficiency by explicitly modeling the intricate biological relationships that other methods overlook. The ongoing development of more robust GNN variants, such as Stable GNNs and Causal GNNs, promises to further bridge the gap between retrospective model performance and reliable, generalizable clinical deployment, ultimately accelerating drug discovery and precision medicine.

In the domain of biomedical data science, graph Neural Networks (GNNs) have emerged as powerful tools for modeling complex biological systems. The performance of these models is profoundly influenced by the foundational step of graph construction, which dictates how entities (like patients or proteins) and the relationships between them are represented. Two predominant strategies are correlation matrices, derived from patterns in empirical data like gene expression, and biological network priors, such as established Protein-Protein Interaction (PPI) networks, which incorporate existing domain knowledge. This guide provides a comparative analysis of these two approaches, contextualized within the broader effort of benchmarking GNNs against other machine learning methods. We synthesize recent experimental evidence to offer researchers and drug development professionals a clear understanding of the trade-offs in accuracy, interpretability, and applicability associated with each method.

---

The table below summarizes key findings from a 2025 study that conducted a head-to-head comparison of GNN models using the two graph construction methods for multi-omics cancer classification on a dataset of 8,464 samples from 31 cancer types [20].

• Model Definitions:

  • LASSO-MOGCN: Multi-Omics Graph Convolutional Network with LASSO feature selection.
  • LASSO-MOGAT: Multi-Omics Graph Attention Network with LASSO feature selection.
  • LASSO-MOGTN: Multi-Omics Graph Transformer Network with LASSO feature selection.

• Performance Comparison:

Graph Construction Method	Model	Overall Accuracy	Key Strengths & Limitations
Patient Correlation Matrix	LASSO-MOGCN	94.70%	Captures patient-specific relationships, enhancing identification of shared cancer signatures [20].
	LASSO-MOGAT	95.90%	Superior performance; attention mechanism effectively weights important relationships in empirical data [20].
	LASSO-MOGTN	94.10%	Leverages transformer architecture to model long-range dependencies within the patient population [20].
PPI Network Prior	LASSO-MOGCN	92.59%	Constrained by existing biological knowledge; may miss novel or cancer-specific interactions not in the database [20].
	LASSO-MOGAT	93.17%	Outperforms other GNNs on PPI graphs but is still less accurate than its correlation-based counterpart [20].
	LASSO-MOGTN	92.21%	Performance limited by the static and potentially incomplete nature of the prior network [20].

The data consistently demonstrates that correlation-based graph structures yielded higher accuracy for this specific task of cancer classification from multi-omics data [20]. The study concluded that these structures better enhance the model's ability to identify shared cancer-specific signatures across patients.

---

Experimental Protocols: A Detailed Look

To ensure reproducibility and provide a clear framework for benchmarking, here are the detailed methodologies for the key experiments cited.

This protocol outlines the experiment that generated the comparative data in the table above.

• Data Acquisition & Preprocessing:

  • Collect multi-omics data (mRNA, miRNA, DNA methylation) from 8,464 samples spanning 31 cancer types and normal tissue.
  • Perform standard normalization and batch effect correction on each omics dataset.
  • Apply LASSO (Least Absolute Shrinkage and Selection Operator) regression for feature selection to reduce data dimensionality and mitigate overfitting.

• Graph Construction:

  • Correlation-Based Graph: For each omics data type, compute a patient similarity network using a correlation metric (e.g., Pearson correlation). A graph is constructed where nodes represent patients, and edges are weighted based on the correlation strength between their omics profiles.
  • PPI-Based Graph: Construct a knowledge-driven graph where nodes represent genes or proteins. Edges are established based on interactions from a curated PPI database (e.g., STRING). Patient omics data is then mapped as features onto the corresponding gene/protein nodes.

• Model Training & Evaluation:

  • Implement three GNN architectures (GCN, GAT, GTN) for both graph types.
  • Train models in a supervised manner for the task of cancer type classification.
  • Evaluate performance using a rigorous cross-validation strategy and report metrics including accuracy, precision, recall, and F1-score.

This protocol describes a novel benchmark suite designed to evaluate PPI prediction models beyond pairwise accuracy, assessing their ability to reconstruct biologically meaningful networks.

• Dataset Curation:

  • Compile a "golden-standard" dataset of high-confidence physical PPIs from multiple sources (STRING, UniProt, Reactome, IntAct) across four organisms (Human, Yeast, E. coli, Arabidopsis thaliana).
  • Apply strict filters to minimize data redundancy and prevent data leakage, resulting in a graph of 21,484 proteins and 186,818 interactions.

• Evaluation Paradigms:

  • Topology-Oriented Tasks:
    • Intra-species PPI Network Construction: Evaluate how well a predicted PPI network matches the ground-truth network's structural properties, such as sparsity, degree distribution, and community structure.
    • Cross-species PPI Network Construction: Assess the model's ability to transfer knowledge and accurately reconstruct the PPI network of one species using a model trained on another.
  • Function-Oriented Tasks:
    • Protein Complex & Pathway Prediction: Measure the functional coherence of predicted interaction modules against known complexes and pathways.
    • GO Module Analysis: Perform Gene Ontology enrichment analysis on predicted modules to check alignment with biological functions.
    • Essential Protein Justification: Test if the reconstructed network can correctly identify proteins known to be essential for survival based on their network topology.

• Key Insight: This benchmark revealed that many state-of-the-art PPI prediction models, while accurate at predicting isolated pairs, generate overly dense networks that poorly recapitulate the sparse, modular topology of real interactomes and show limited functional alignment [77].

Experimental Workflow and Decision Framework

The following diagram illustrates the logical workflow and key decision points when choosing between correlation matrices and biological priors for graph construction.

The Scientist's Toolkit: Essential Research Reagents

Successful experimentation in this field relies on several key resources. The table below lists essential "research reagents," including datasets, software, and databases.

Item Name	Type	Function & Explanation
STRING Database [78] [79]	Biological Database	A comprehensive resource of known and predicted Protein-Protein Interactions, both physical and functional. Used as a prior biological network [78].
CausalBench Suite [80]	Benchmarking Software	An open-source benchmark suite for evaluating network inference methods on real-world, large-scale single-cell perturbation data. Critical for rigorous model validation [80].
PRING Benchmark [77]	Benchmarking Dataset & Tools	The first comprehensive benchmark to evaluate PPI prediction models from a graph-level perspective, assessing both topological and functional network recovery [77].
BioJS Components [81]	Visualization Library	A suite of open-source JavaScript components, including force-directed and circular layouts, for the web-based visualization of PPI networks without browser plugins [81].
Cytoscape [82]	Desktop Application	A powerful, stand-alone software platform for visualizing complex molecular interaction networks and integrating these with other types of data [82].

---

Discussion and Research Outlook

The empirical evidence indicates that the choice between correlation matrices and biological network priors is not one of superiority but of contextual fitness. Correlation matrices excel in discriminative tasks like patient classification, where data-driven patterns are paramount [20]. In contrast, biological priors provide an essential scaffold for generative and discovery-oriented tasks, such as reconstructing the full human interactome or elucidating novel disease pathways, where grounding in established biology is crucial [78] [77].

A significant challenge with PPI network priors is their potential for being incomplete or static, which can limit the discovery of novel, context-specific interactions [20] [77]. The emerging trend, as seen in models like HIGH-PPI [78] and MESM [79], is hybrid integration. These approaches combine multiple views—for instance, using a PPI network as a topological backbone (top view) while enriching node features with detailed, data-driven protein representations (bottom view). This synergy allows GNNs to leverage both existing knowledge and learn novel patterns from high-throughput data.

Future research will likely focus on dynamic graph construction, where networks evolve based on conditional data, and standardized benchmarking using frameworks like PRING [77] to move beyond pairwise accuracy toward a more holistic, network-level understanding of model performance. For researchers benchmarking GNNs, the critical takeaway is to align the graph construction methodology not just with the immediate predictive task, but with the ultimate biological question being asked.

The advent of high-throughput sequencing technologies has enabled the comprehensive profiling of biological systems across multiple molecular layers, or 'omics'. While single-omics analyses have provided valuable insights, integrating these diverse data types presents an opportunity to achieve a more holistic understanding of complex disease mechanisms. This guide quantitatively assesses the performance advantage of multi-omics data integration over single-omics models, with a specific focus on benchmarking Graph Neural Networks (GNNs) against other machine learning approaches in biomedical research. The evidence presented demonstrates that integrated models consistently outperform their single-omics counterparts in critical tasks such as disease classification and biomarker discovery.

Performance Comparison: Multi-Omics vs. Single-Omics Models

Quantitative Performance Gains

The table below summarizes key experimental results from recent studies, directly comparing the performance of multi-omics integration models against single-omics approaches.

Table 1: Performance Comparison of Multi-Omics vs. Single-Omics Models

Study and Model	Task	Single-Omics Performance	Multi-Omics Performance	Performance Gain
LASSO-MOGAT [20]	31-type Cancer Classification (Accuracy)	DNA Methylation alone: 94.88%mRNA + DNA Methylation: 95.67%	mRNA + miRNA + DNA Methylation: 95.90%	+1.02%
GNNRAI Framework [19]	Alzheimer's Disease Classification (Avg. Accuracy across 16 Biodomains)	Unimodal (Transcriptomics/Proteomics) Baselines	Integrated Multi-Omics: +2.2% Accuracy	+2.2%
GNN-Suite Benchmark [2]	Cancer-Driver Gene Identification (Balanced Accuracy)	Logistic Regression (Baseline)	Best GNN (GCN2 on STRING network): 0.807 +/- 0.035	Notable (exact baseline not provided)

Key Findings from Comparative Data

• Incremental Improvement with Added Modalities: The performance of graph-based models increases as more omics layers are integrated. For instance, LASSO-MOGAT showed a clear performance gradient, with accuracy improving from a single omic (94.88%) to two omics (95.67%) and peaking with three omics (95.90%) [20].
• Superiority of Advanced GNN Architectures: Among GNNs, the Graph Attention Network (GAT) demonstrated the best overall performance in multi-omics cancer classification, leveraging its ability to assign different weights to neighboring nodes in a graph, which is crucial for handling heterogeneous biological data [20].
• Advantage over Non-GNN Baselines: Benchmarking studies confirm that various GNN architectures (GAT, GCN, GTN) consistently outperform a standard logistic regression baseline, underscoring the value of network-based learning over feature-only approaches [2].

Detailed Experimental Protocols

To ensure reproducibility and provide clarity on how the quantitative results were achieved, this section outlines the key methodologies from the cited studies.

This protocol describes the experiment for the LASSO-MOGAT model, which achieved 95.9% accuracy.

• 1. Data Preparation and Feature Selection:
  • Data: 8,464 samples from 31 cancer types and normal tissue, comprising mRNA, miRNA, and DNA methylation data.
  • Feature Selection: LASSO (Least Absolute Shrinkage and Selection Operator) regression was employed for dimensionality reduction and feature selection to handle the high dimensionality of the omics data.
• 2. Graph Structure Construction:
  • Two types of graph structures were investigated to model relationships between biological entities:
    • Correlation-based Graphs: Built using sample correlation matrices to capture shared cancer-specific signatures across patients.
    • Biological Knowledge Graphs: Constructed from Protein-Protein Interaction (PPI) networks (e.g., from STRING and BioGRID) to capture known biological interactions.
• 3. Model Training and Evaluation:
  • Models: Three GNN architectures—Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Transformer Network (GTN)—were trained and compared.
  • Training: Models were developed to perform node classification on the constructed graphs.
  • Validation: Experimental results demonstrated that correlation-based graph structures generally enhanced model performance compared to PPI-based graphs for this specific task.

This protocol details the GNNRAI framework, which showed an average 2.2% accuracy improvement over unimodal models.

• 1. Data and Prior Knowledge Integration:
  • Data: Transcriptomic and proteomic data from the ROSMAP cohort for Alzheimer's disease (AD) classification.
  • Biological Priors: Instead of patient similarity networks, the framework used prior knowledge from AD biological domains (Biodomains). These are functional units in the transcriptome/proteome reflecting AD-associated endophenotypes.
  • Graph Construction: For each biodomain, a knowledge graph was created where nodes represent genes/proteins, and edges represent co-expression relationships derived from the Pathway Commons database.
• 2. Model Architecture and Training:
  • GNN Feature Extraction: Each sample's omics data (for a given biodomain) is represented as a graph. A GNN processes each graph to produce a low-dimensional embedding.
  • Representation Alignment and Integration: The modality-specific embeddings are aligned to enforce shared patterns and then integrated using a set transformer.
  • Handling Missing Data: The architecture allows for incorporation of samples with incomplete omics measurements (e.g., missing proteomics data), preventing a reduction in statistical power.
• 3. Explainability and Biomarker Identification:
  • The method of integrated gradients was applied to the trained model to identify the most informative genes/proteins (biomarkers) for AD prediction, leveraging the incorporated biological pathway knowledge.

Experimental Workflow and Signaling Pathways

The following diagram illustrates the core logical workflow of a multi-omics integration study using graph-based methods, synthesizing the common elements from the described protocols.

Multi-Omics GNN Integration Workflow

The Scientist's Toolkit: Essential Research Reagents & Materials

The table below catalogs key computational tools, data resources, and model architectures essential for conducting multi-omics integration studies, as featured in the benchmarked experiments.

Table 2: Key Research Reagent Solutions for Multi-Omics Integration

Item Name	Type	Primary Function in Research	Example Use Case
Graph Neural Network (GNN)	Model Architecture	Analyzes data structured as graphs, capturing complex relationships between biological entities (e.g., genes, patients) [20] [19].	Core learning model for node or graph-level prediction tasks.
GNN-Suite [2]	Benchmarking Framework	A modular Nextflow-based framework for fair and reproducible benchmarking of diverse GNN architectures (e.g., GAT, GCN, GTN) in computational biology.	Standardized evaluation of GNN performance on tasks like cancer-driver gene identification.
Protein-Protein Interaction (PPI) Networks	Biological Knowledge Base	Provides prior biological knowledge for graph construction, using known protein interactions to define edges between molecular features [20] [2].	Building biological knowledge graphs (e.g., from STRING, BioGRID).
Biological Domains (Biodomains) [19]	Biological Knowledge Base	Functional units (e.g., pathways) reflecting disease-associated endophenotypes. Used as a structured prior to group features and build meaningful graphs.	Creating focused, biologically relevant graphs for Alzheimer's disease classification.
LASSO Regression [20]	Statistical Method	Performs feature selection and regularization to handle high-dimensional omics data by shrinking less important feature coefficients to zero.	Dimensionality reduction of omics data (mRNA, miRNA, methylation) before model training.
Pathway Commons [19]	Biological Knowledge Base	A centralized resource of publicly available biological pathway data from multiple databases, used to query molecular interactions.	Sourcing co-expression relationships and interactions to build biodomain knowledge graphs.
Integrated Gradients [19]	Explainability Method	An attribution method that uses model gradients to estimate the contribution of each input feature to a prediction, enhancing model interpretability.	Identifying and ranking informative biomarkers (genes/proteins) from a trained GNN model.

The empirical evidence from recent benchmarking studies unequivocally quantifies the multi-omics advantage. The integration of diverse molecular data types through advanced computational methods, particularly Graph Neural Networks, consistently delivers superior performance in critical biomedical tasks like cancer and Alzheimer's disease classification compared to single-omics models. Key factors contributing to this advantage include the use of attention mechanisms (as in GATs), the incorporation of structured biological prior knowledge (from PPI networks or biodomains), and robust methods for handling high-dimensionality and data heterogeneity. As the field progresses, frameworks like GNN-Suite promise to further standardize benchmarking efforts, guiding researchers and drug development professionals toward the most effective integration strategies for precision medicine.

Conclusion

The benchmarking evidence consistently shows that GNNs offer a significant performance advantage over traditional ML for many biomedical tasks, particularly those involving inherent relational structures. Success hinges on thoughtful graph construction, whether based on biological knowledge or data-driven similarity, and on addressing key challenges in generalization and causality. Future progress will be driven by the development of more robust, causally-aware GNN architectures, standardized benchmarking practices, and frameworks for integrating large language models. This will ultimately pave the way for reliable Causal Digital Twins and in silico clinical experimentation, fundamentally accelerating drug discovery and precision medicine.

References

• 1. How Knowledge Graphs Are Transforming Biomedical ... [https://www.thinkbio.ai/resources/how-knowledge-graphs-are-reshaping-biomedical-research-and-healthcare/]
• 2. GNN-Suite: a Graph Neural Network Benchmarking ... [https://arxiv.org/abs/2505.10711]
• 3. A comprehensive large-scale biomedical knowledge graph ... [https://www.nature.com/articles/s42256-025-01014-w]
• 4. Knowledge Graphs in Biomedicine: Unlocking Biomedical ... [https://www.elucidata.io/blog/biomedical-knowledge-graphs]
• 5. Graph Representation Learning in Biomedicine and Healthcare [https://pmc.ncbi.nlm.nih.gov/articles/PMC10699434/]
• 6. Graph Neural Networks and Their Current Applications in ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.690049/full]
• 7. PT-KGNN: A framework for pre-training biomedical ... [https://www.sciencedirect.com/science/article/abs/pii/S0010482524008539]
• 8. Graph neural networks learn emergent tissue properties ... [https://www.nature.com/articles/s41467-025-63758-8]
• 9. Graph neural networks driven acceleration in drug discovery [https://www.sciencedirect.com/science/article/pii/S2211383525006884]
• 10. Kolmogorov–Arnold graph neural networks for molecular ... [https://www.nature.com/articles/s42256-025-01087-7]
• 11. Geometry-enhanced graph neural networks accelerate ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12277138/]
• 12. Causal Graph Neural Networks for Healthcare [https://arxiv.org/html/2511.02531v1]
• 13. Improving the accuracy of medical diagnosis with causal ... [https://www.nature.com/articles/s41467-020-17419-7]
• 14. Graph Neural Networks and Their Current Applications in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8360394/]
• 15. A Graph Neural Network bench-marking framework for ... [https://arxiv.org/html/2505.10711v1]
• 16. Edges are all you need: Potential of medical time series ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12237013/]
• 17. Self-Explainable Graph Neural Network for Alzheimer Disease ... [https://aging.jmir.org/2024/1/e54748/]
• 18. Introducing a Graph Neural Network Benchmark in MLPerf ... [https://mlcommons.org/2025/04/rgat-inference-v5/]
• 19. Effective integration of multi-omics with prior knowledge to ... [https://www.nature.com/articles/s41540-025-00519-9]
• 20. Comparative Analysis of Multi-Omics Integration Using ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11928009/]
• 21. SynOmics: integrating multi-omics data through feature ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12613832/]
• 22. Deep learning-based approaches for multi-omics data ... [https://biodatamining.biomedcentral.com/articles/10.1186/s13040-024-00391-z]
• 23. AI Tools Every Biomedical Should Know in Researcher 2025 [https://irrespub.com/ai-tools-biomedical-research-2025/]
• 24. Recent Developments in GNNs for Drug Discovery [https://arxiv.org/html/2506.01302v1]
• 25. Could graph neural networks learn better molecular ... [https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00479-8]
• 26. Enhancing molecular property prediction with quantized GNN ... [https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-00989-3]
• 27. Using GNN property predictors as molecule generators [https://www.nature.com/articles/s41467-025-59439-1]
• 28. Causal Graph Neural Networks for Healthcare [https://arxiv.org/html/2511.02531v2]
• 29. Molecular representation learning: cross-domain foundations ... [https://pubs.rsc.org/en/content/articlehtml/2025/dd/d5dd00170f]
• 30. Similarity-Based Self-Construct Graph Model for Predicting ... [https://arxiv.org/html/2508.00615v1]
• 31. Machine Learning-Based Mortality Risk Prediction Model in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12101463/]
• 32. Development and validation of an interpretable model for ... [https://www.nature.com/articles/s41598-024-64463-0]
• 33. Intelligent Prediction Platform for Sepsis Risk Based on Real ... [https://medinform.jmir.org/2025/1/e74940]
• 34. Preprocessing Structured Clinical Data for Predictive ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5228148/]
• 35. Revolutionizing sepsis diagnosis using machine learning and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12548141/]
• 36. Quantitative evaluation of explainable graph neural ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9782255/]
• 37. Fine-grained Patient Similarity Measuring using ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11654828/]
• 38. Time-Aware Attention for Enhanced Electronic Health ... [https://arxiv.org/html/2507.14847v1]
• 39. Graph Neural Networks in Cancer and Oncology Research [https://pmc.ncbi.nlm.nih.gov/articles/PMC10742144/]
• 40. Biologically explainable multi-omics feature demonstrates ... [https://www.nature.com/articles/s41598-025-24352-6]
• 41. Comparative analysis of statistical and deep learning ... [https://pubmed.ncbi.nlm.nih.gov/40598554/]
• 42. Amogel: a multi-omics classification framework using ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06111-6]
• 43. Integration of Graph Neural Networks and multi-omics ... [https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-024-05976-0]
• 44. Benchmarking machine learning methods for synthetic ... [https://www.nature.com/articles/s41467-024-52900-7]
• 45. Integrative multi-omics analysis and experimental ... [https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1629683/full]
• 46. Agendia to Reveal Novel Breast Cancer Outcome Data ... [https://agendia.com/agendia-to-reveal-novel-breast-cancer-outcome-data-identifying-genomic-risk-and-treatment-disparities-among-diverse-patient-population-at-2025-asco-annual-meeting/]
• 47. A framework for pre-training biomedical knowledge graphs ... [https://pubmed.ncbi.nlm.nih.gov/38936076/]
• 48. Learning universal knowledge graph embedding for ... [https://pubmed.ncbi.nlm.nih.gov/41052159/]
• 49. BALI: Enhancing Biomedical Language Representations ... [https://arxiv.org/html/2509.07588v1]
• 50. Knowledge Graph Embeddings in the Biomedical Domain [https://medium.com/@EleventhHourEnthusiast/knowledge-graph-embeddings-in-the-biomedical-domain-are-they-useful-9907978fe5aa]
• 51. Diversifying Knowledge Enhancement of Biomedical ... [https://arxiv.org/html/2312.13881v1]
• 52. A comparative analysis of three graph neural network ... [https://www.nature.com/articles/s41598-025-97257-z]
• 53. Stable clinical risk prediction against distribution shift in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10499849/]
• 54. Distribution shift detection for the postmarket surveillance ... [https://www.nature.com/articles/s41746-024-01085-w]
• 55. Fusion-driven multimodal learning for biomedical time series ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12484004/]
• 56. A machine learning approach for multimodal data fusion ... [https://www.nature.com/articles/s41698-025-00917-6]
• 57. A graph neural network model for inferring interindividual ... [https://www.nature.com/articles/s41598-025-23320-4]
• 58. Fraud detection and explanation in medical claims using ... [https://www.nature.com/articles/s41598-025-22910-6]
• 59. Cross-attention graph neural networks for inferring gene ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-025-06186-1]
• 60. Edge-relational window-attentional graph neural network ... [https://www.sciencedirect.com/science/article/abs/pii/S001048252400533X]
• 61. ScaleGNN: Towards Scalable Graph Neural Networks via ... [https://arxiv.org/html/2504.15920v1]
• 62. How Graph Neural Networks (GNN) Outperform Traditional ... [https://www.tigergraph.com/blog/how-graph-neural-networks-gnn-outperform-traditional-machine-learning/]
• 63. Benchmarking Data Science Agents for Biomedical Research [https://arxiv.org/html/2505.16100v1]
• 64. [Literature Review] Causal Graph Neural Networks for ... [https://www.themoonlight.io/en/review/causal-graph-neural-networks-for-healthcare]
• 65. Causality-aware graph neural networks for functional ... [https://www.nature.com/articles/s41540-025-00567-1]
• 66. CiGNN: A Causality-Informed and Graph Neural Network ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11100861/]
• 67. A lightweight graph neural network to predict long-term ... [https://www.sciencedirect.com/science/article/abs/pii/S1532046425000759]
• 68. Causal Inference, Biomarker Discovery, Graph Neural ... [https://www.arxiv.org/abs/2511.13295]
• 69. Dual-route embedding-aware graph neural networks for drug ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12554636/]
• 70. Introducing GNN-Suite: a benchmarking framework for ... [https://www.linkedin.com/posts/tiansimpson_excited-to-share-our-latest-work-we-introduce-activity-7330146891222966272-F8Ij]
• 71. stracquadaniolab/gnn-suite: Nextflow pipeline to train ... [https://github.com/stracquadaniolab/gnn-suite]
• 72. Gene expression inference based on graph neural ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12161499/]
• 73. Clinical applications of graph neural networks in ... [https://www.sciencedirect.com/science/article/abs/pii/S0010482523006662]
• 74. Research on GNNs with stable learning - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12334600/]
• 75. Methodological and reporting quality of machine learning ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12034563/]
• 76. The Use of Machine Learning for Analyzing Real-World Data ... [https://medinform.jmir.org/2025/1/e68898]
• 77. PRING: Rethinking Protein-Protein Interaction Prediction ... [https://arxiv.org/html/2507.05101v1]
• 78. Hierarchical graph learning for protein–protein interaction [https://www.nature.com/articles/s41467-023-36736-1]
• 79. integrating multi-source data for high-accuracy protein ... [https://pubmed.ncbi.nlm.nih.gov/40784875/]
• 80. A large-scale benchmark for network inference from single- ... [https://www.nature.com/articles/s42003-025-07764-y]
• 81. PPI layouts: BioJS components for the display of Protein- ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4103490/]
• 82. Visualizating and Analysing PPI Networks in Cytoscape [https://cytoscape.org/cytoscape-tutorials/presentations/embo-ppi-2018-rome]