ArticleScientific reports2016
Tissue Specificity of Human Disease Module.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.
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- Pleiotropy and disease interactors: the dual nature of genes linking ageing and ageing-related diseases.Biogerontology · 2026Article
- [Prediction andZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026Article
- Single-Cell Gene Module Inference Reveals Alternative Polyadenylation Dynamics Associated with Autism.International journal of molecular sciences · 2026Article
- Multi-Omics and Single-Cell Omics: New Tools in Drug Target Discovery.Arteriosclerosis, thrombosis, and vascular biology · 2024Article
- Integrative network analysis suggests prioritised drugs for atopic dermatitis.Journal of translational medicine · 2024Article
- Noncoding RNAs improve the predictive power of network medicine.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.Molecular systems biology · 2023Article
- Integrative analysis of transcriptome dynamics during human craniofacial development identifies candidate disease genes.Nature communications · 2023Article
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases.Cell reports. Medicine · 2023Article
- Robust and rigorous identification of tissue-specific genes by statistically extending tau score.BioData mining · 2022Article
- An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research.Cells · 2022Review
- Tissue Specificity of Gene Expression Evolves Across Mammal Species.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Molecular Modelling Hurdle in the Next-Generation Sequencing Era.International journal of molecular sciences · 2022Article
- Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets.BMC bioinformatics · 2022Article
- Shared and Cell-Type-Specific Gene Expression Patterns Associated With Autism Revealed by Integrative Regularized Non-Negative Matrix Factorization.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
Genes carrying mutations associated with genetic diseases are present in all human cells; yet, clinical manifestations of genetic diseases are usually highly tissue-specific. Although some disease genes are expressed only in selected tissues, the expression patterns of disease genes alone cannot explain the observed tissue specificity of human diseases. Here we hypothesize that for a disease to manifest itself in a particular tissue, a whole functional subnetwork of genes (disease module) needs to be expressed in that tissue. Driven by this hypothesis, we conducted a systematic study of the expression patterns of disease genes within the human interactome. We find that genes expressed in a specific tissue tend to be localized in the same neighborhood of the interactome. By contrast, genes expressed in different tissues are segregated in distinct network neighborhoods. Most important, we show that it is the integrity and the completeness of the expression of the disease module that determines disease manifestation in selected tissues. This approach allows us to construct a disease-tissue network that confirms known and predicts unexpected disease-tissue associations.
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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 OpenQuestion graph.