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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL

Tao Wang¹, Qidi Peng¹, Bo Liu¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Frontiers in Bioengineering and Biotechnology

|May 22, 2020

Summary

This study introduces N2V-HC, an unbiased method to find scattered disease gene modules using deep learning on biological networks. It improves the discovery of pathways and drug targets for complex diseases.

Keywords:

GWAS disease module identification eQTL hierarchical clustering node2vec

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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Published on: August 20, 2019

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Identifying disease-relevant gene modules is crucial for understanding disease pathways and discovering drug targets.
Existing methods often focus on well-studied genes, leading to biased discovery of single subnetworks.
Disease proteins are known to form scattered, connected components within protein-protein interaction networks.

Purpose of the Study:

To develop an unbiased algorithm framework, N2V-HC, for discovering scattered disease modules.
To leverage deep representation learning on integrated multi-layer biological networks.
To improve the identification of disease pathways and potential therapeutic targets.

Main Methods:

N2V-HC predicts disease-associated genes using Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) data.
An integrated network is constructed from the human interactome, with node features learned via deep representation learning.
Hierarchical clustering with dynamic tree cut identifies modules enriched with disease-associated genes.

Main Results:

N2V-HC demonstrates superior performance in network module discovery compared to existing methods on both real and simulated networks.
The algorithm successfully identifies biologically meaningful modules related to complex disease pathways.
Case studies on Parkinson's and Alzheimer's diseases highlight the method's utility.

Conclusions:

N2V-HC provides an effective and unbiased approach for discovering scattered disease modules.
The method enhances the identification of disease pathways and potential drug targets for complex diseases.
Deep representation learning on integrated biological networks is a powerful strategy for network module discovery.