ArticleProceedings of the National Academy of Sciences of the United States of America2006
Bayesian error analysis model for reconstructing transcriptional regulatory networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.Nucleic acids research · 2016Article
- Integrative Approaches for microRNA Target Prediction: Combining Sequence Information and the Paired mRNA and miRNA Expression Profiles.Current bioinformatics · 2013Article
- HIDEN: Hierarchical decomposition of regulatory networks.BMC bioinformatics · 2012Article
- Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.Scientific reports · 2012Article
- Bayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription Factor.The annals of applied statistics · 2012Article
- A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer.BMC systems biology · 2011Article
- Sparse Regulatory Networks.The annals of applied statistics · 2010Article
- Monitoring the regulation of gene expression in a growing organ using a fluid mechanics formalism.BMC biology · 2010Article
- A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data.Statistics in medicine · 2010Article
- Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53.BMC bioinformatics · 2010Article
- Revealing a signaling role of phytosphingosine-1-phosphate in yeast.Molecular systems biology · 2010Article
- ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells.Proceedings of the National Academy of Sciences of the United States of America · 2009Article
- Reconstructing transcriptional regulatory networks through genomics data.Statistical methods in medical research · 2009Article
- Modeling post-transcriptional regulation activity of small non-coding RNAs in Escherichia coli.BMC bioinformatics · 2009Article
- Analysis of time-series gene expression data: methods, challenges, and opportunities.Annual review of biomedical engineering · 2007Article
- Connectivity in the yeast cell cycle transcription network: inferences from neural networks.PLoS computational biology · 2006Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical-chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse types of genomic data that reveal valuable information on transcription regulation, including DNA sequence data, protein-DNA binding data, microarray gene expression data, and others. In this article, we propose a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. There are two unique aspects to this proposed model. First, transcription is modeled as a set of biochemical reactions, and a linear system model with clear biological interpretation is developed. Second, measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework. Model parameters are inferred through Markov chain Monte Carlo. The usefulness of this approach is demonstrated through its application to infer transcriptional regulatory networks in the yeast cell cycle.
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