ArticleBMC systems biology2014
Inferring transcription factor collaborations in gene regulatory networks.
Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Probing transcription factor subsets in gene regulatory networks.Algorithms for molecular biology : AMB · 2026Article
- Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.Genetics · 2020Article
- Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits.Genetics · 2019Article
- Characterization of transcription factor response kinetics in parallel.BMC biotechnology · 2016Article
- Iron acquisition and oxidative stress response in aspergillus fumigatus.BMC systems biology · 2015Article
- Inferring interaction type in gene regulatory networks using co-expression data.Algorithms for molecular biology : AMB · 2015Article
- Not Just a Sum? Identifying Different Types of Interplay between Constituents in Combined Interventions.PloS one · 2015Article
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Authors and funding
2 authors.
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Abstract
backgroundLiving cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation.
resultsWe model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones.
conclusionsmTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM.
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