ArticleNPJ systems biology and applications2021
Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed, 87 citations in OpenAlex.
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- Network-based drug repurposing for psychiatric disorders using single-cell genomics.Cell genomics · 2025Article
- TransMarker: Unveiling dynamic network biomarkers in cancer progression through cross-state graph alignment and optimal transport.PLoS computational biology · 2025Article
- Role of microRNAs in chronic hepatitis E viral infection.Bioinformation · 2025Article
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- scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.Cell reports. Medicine · 2024Article
- TTD: Therapeutic Target Database describing target druggability information.Nucleic acids research · 2024Article
- In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma.Frontiers in oncology · 2024Article
- Cross-linking breast tumor transcriptomic states and tissue histology.Cell reports. Medicine · 2023Article
- Regulation of expression quantitative trait loci by SVA retrotransposons within the major histocompatibility complex.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Navigating the Gene Co-Expression Network and Drug Repurposing Opportunities for Brain Disorders Associated with Neurocognitive Impairment.Brain sciences · 2023Article
- Ecological network analysis reveals cancer-dependent chaperone-client interaction structure and robustness.Nature communications · 2023Article
- Circadian Regulation of the Neuroimmune Environment Across the Lifespan: From Brain Development to Aging.Journal of biological rhythms · 2023Review
- R-Mixup: Riemannian Mixup for Biological Networks.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2023Article
- Uncovering Protein Networks in Cardiovascular Proteomics.Molecular & cellular proteomics : MCP · 2023Review
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein-protein interaction network (PPI, or interactome) to predict novel disease-disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.
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Registered trials
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 OpenQuestion graph.