engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection

2026-01-20

arXiv.org

AI Summary

This study addresses the challenges posed by high-dimensional omics data—characterized by small sample sizes, high dimensionality, and complex biological network relationships—in disease classification and biomarker identification. To this end, the authors propose engGNN, a dual-graph neural network framework that uniquely integrates external prior biological networks with a data-driven directed graph derived from tree ensemble models, forming a complementary dual-graph architecture. By leveraging a dual-graph fusion mechanism to enhance embedding representations, engGNN achieves significantly improved classification accuracy across multiple real-world gene expression datasets. Furthermore, the method enables feature importance scoring and pathway enrichment analysis, thereby balancing predictive performance with biological interpretability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph Neural Networks (GNNs) offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated one, which limits their ability to capture complementary information. To address this, we propose the external and generated Graph Neural Network (engGNN), a dual-graph framework that jointly leverages both external known biological networks and data-driven generated graphs. Specifically, engGNN constructs a biologically informed undirected feature graph from established network databases and complements it with a directed feature graph derived from tree-ensemble models. This dual-graph design produces more comprehensive embeddings, thereby improving predictive performance and interpretability. Through extensive simulations and real-world applications to gene expression data, engGNN consistently outperforms state-of-the-art baselines. Beyond classification, engGNN provides interpretable feature importance scores that facilitate biologically meaningful discoveries, such as pathway enrichment analysis. Taken together, these results highlight engGNN as a robust, flexible, and interpretable framework for disease classification and biomarker discovery in high-dimensional omics contexts.

Problem

• omics data
• disease classification
• feature selection
• high dimensionality
• biological networks

Innovation

• dual-graph neural network
• omics data integration
• biological network
• feature selection
• interpretable GNN