ArticleBioinformatics (Oxford, England)2010
Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Estimating gene regulatory networks with pandaR.Bioinformatics (Oxford, England) · 2017Article
- Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer.Nucleic acids research · 2017Article
- Finding New Order in Biological Functions from the Network Structure of Gene Annotations.PLoS computational biology · 2015Article
- Sexually-dimorphic targeting of functionally-related genes in COPD.BMC systems biology · 2014Article
- Article
- Computational methodology for ChIP-seq analysis.Quantitative biology (Beijing, China) · 2013Article
- Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.Frontiers in genetics · 2013Article
- Article
- Cell-type specificity of ChIP-predicted transcription factor binding sites.BMC genomics · 2012Article
- Implications of functional similarity for gene regulatory interactions.Journal of the Royal Society, Interface · 2012Article
- LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data.Nucleic acids research · 2011Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
resultsWe have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression data. The network is specified by a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood. We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation. AVAILABILITY: Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip
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