ArticlePLoS genetics2014
Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association.
Article in PLoS genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 36 citations in OpenAlex.
- Transcriptional regulatory network differences between six industrialMetabolic engineering communications · 2026Article
- Exploring potential transcription factors and their regulatory relationships based on asymmetric covariance natural vector encoding method and machine learning algorithms.Briefings in bioinformatics · 2026Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.Nucleic acids research · 2022Article
- Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes.Nucleic acids research · 2022Article
- Optimal dimensionality selection for independent component analysis of transcriptomic 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
- Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling.Cancers · 2020Article
- The Escherichia coli transcriptome mostly consists of independently regulated modules.Nature communications · 2019Article
- Article
- A comprehensive evaluation of module detection methods for gene expression data.Nature communications · 2018Article
- Genomic copy number variation analysis in multiple system atrophy.Molecular brain · 2017Article
- Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases.Nature methods · 2016Article
- Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes.BMC bioinformatics · 2016Article
- Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets.PloS one · 2016Article
- The genetic regulatory network centered on Pto-Wuschela and its targets involved in wood formation revealed by association studies.Scientific reports · 2015Article
- TRRUST: a reference database of human transcriptional regulatory interactions.Scientific reports · 2015Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.
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