ArticleBriefings in functional genomics2010
Gene set enrichment; a problem of pathways.
Article in Briefings in functional genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Type I interferon-dependent and -independent signaling underlie autoantibody production in a murine lupus model.Journal of translational autoimmunity · 2026Article
- Alzheimer's Disease Risk AllelebioRxiv : the preprint server for biology · 2026Article
- Exercise attenuates high-fat diet-induced liver injury in mice via PPARα pathway and reduction of oxidative stress and inflammation.Journal of physiology and biochemistry · 2026Article
- Integrated transcriptomic and immune-associated network analysis of breast cancer patient-derived organoids reveals candidate inflammatory biomarkers.Frontiers in bioengineering and biotechnology · 2026Article
- Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets.Archives of toxicology · 2023Article
- PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing.BMC bioinformatics · 2023Article
- Gene-set Enrichment with Mathematical Biology (GEMB).GigaScience · 2020Article
- IPAVS: Integrated Pathway Resources, Analysis and Visualization System.Nucleic acids research · 2012Article
- Time series gene expression profiling and temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization.BMC systems biology · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Gene Set Enrichment (GSE) is a computational technique which determines whether a priori defined set of genes show statistically significant differential expression between two phenotypes. Currently, the gene sets used for GSE are derived from annotation or pathway databases, which often contain computationally based and unrepresentative data. Here, we propose a novel approach for the generation of comprehensive and biologically derived gene sets, deriving sets through the application of machine learning techniques to gene expression data. These gene sets can be produced for specific tissues, developmental stages or environments. They provide a powerful and functionally meaningful way in which to mine genomewide association and next generation sequencing data in order to identify disease-associated variants and pathways.
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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.