ArticleFrontiers in bioengineering and biotechnology2020
Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- GNN4DM: a graph neural network-based method to identify overlapping functional disease modules.Bioinformatics (Oxford, England) · 2024Article
- Insights into preeclampsia: a bioinformatics approach to deciphering genetic and immune contributions.Frontiers in genetics · 2024Article
- Integration of biological data via NMF for identification of human disease-associated gene modules through multi-label classification.PloS one · 2024Article
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Article
- Editorial: Exploring GWAS data by biomolecular network analysis in revealing genetic disease mechanisms.Frontiers in genetics · 2023Article
- DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions.Bioinformatics (Oxford, England) · 2023Article
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- An interolog-based barley interactome as an integration framework for immune signaling.Genetics · 2022Article
- How Far Are We from the Completion of the Human Protein Interactome Reconstruction?Biomolecules · 2022Article
- A pipeline for RNA-seq based eQTL analysis with automated quality control procedures.BMC bioinformatics · 2021Article
- Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning.Frontiers in genetics · 2021Article
- Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of disease-relevant gene modules is one of the main methods to discover disease pathway and potential drug targets. Recent studies have found that most disease proteins tend to form many separate connected components and scatter across the protein-protein interaction network. However, most of the research on discovering disease modules are biased toward well-studied seed genes, which tend to extend seed genes into a single connected subnetwork. In this paper, we propose N2V-HC, an algorithm framework aiming to unbiasedly discover the scattered disease modules based on deep representation learning of integrated multi-layer biological networks. Our method first predicts disease associated genes based on summary data of Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) studies, and generates an integrated network on the basis of human interactome. The features of nodes in the network are then extracted by deep representation learning. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module discovery. Case studies on Parkinson's disease and Alzheimer's disease, show that N2V-HC can be used to discover biological meaningful modules related to the pathways underlying complex diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.