ArticleJournal of biomedical informatics2010
Independent component analysis: mining microarray data for fundamental human gene expression modules.
Article in Journal of biomedical informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 111 citations in OpenAlex.
- Interpretation of T cell states from single-cell transcriptomics data using reference atlases.Nature communications · 2021Pooled it
- Background gene expression networks significantly enhance drug response prediction by transcriptional profiling.The pharmacogenomics journal · 2012Pooled it
- Transcriptional regulatory network differences between six industrialMetabolic engineering communications · 2026Article
- STRIVE: interpretable multimodal subtype discovery in Women's cancers using an attention-weighted joint variational autoencoder.Frontiers in bioinformatics · 2026Article
- A primer on correlation-based dimension reduction methods for multi-omics analysis.Journal of the Royal Society, Interface · 2023Review
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- Causal Discovery in High-dimensional, Multicollinear Datasets.Frontiers in epidemiology · 2022Article
- Optimal dimensionality selection for independent component analysis of transcriptomic data.BMC bioinformatics · 2021Article
- GEOlimma: differential expression analysis and feature selection using pre-existing microarray data.BMC bioinformatics · 2021Article
- Independent component analysis recovers consistent regulatory signals from disparate datasets.PLoS computational biology · 2021Article
- iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.Nucleic acids research · 2021Article
- Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.Nature communications · 2020Article
- Application of Transcriptional Gene Modules to Analysis ofG3 (Bethesda, Md.) · 2020Article
- Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations.Genome biology · 2020Article
- The Escherichia coli transcriptome mostly consists of independently regulated modules.Nature communications · 2019Article
- Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets.International journal of molecular sciences · 2019Review
- Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis.Molecular & cellular proteomics : MCP · 2019Article
- Molecular Inverse Comorbidity between Alzheimer's Disease and Lung Cancer: New Insights from Matrix Factorization.International journal of molecular sciences · 2019Article
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
As public microarray repositories rapidly accumulate gene expression data, these resources contain increasingly valuable information about cellular processes in human biology. This presents a unique opportunity for intelligent data mining methods to extract information about the transcriptional modules underlying these biological processes. Modeling cellular gene expression as a combination of functional modules, we use independent component analysis (ICA) to derive 423 fundamental components of human biology from a 9395-array compendium of heterogeneous expression data. Annotation using the Gene Ontology (GO) suggests that while some of these components represent known biological modules, others may describe biology not well characterized by existing manually-curated ontologies. In order to understand the biological functions represented by these modules, we investigate the mechanism of the preclinical anti-cancer drug parthenolide (PTL) by analyzing the differential expression of our fundamental components. Our method correctly identifies known pathways and predicts that N-glycan biosynthesis and T-cell receptor signaling may contribute to PTL response. The fundamental gene modules we describe have the potential to provide pathway-level insight into new gene expression datasets.
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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.