ArticleThe annals of applied statistics2012
Bayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription Factor.
Article in The annals of applied statistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.Patterns (New York, N.Y.) · 2023Review
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- DNLC: differential network local consistency analysis.BMC bioinformatics · 2019Article
- Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors.BMC bioinformatics · 2017Article
- Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks.Journal of molecular cell biology · 2017Article
- Detecting subnetwork-level dynamic correlations.Bioinformatics (Oxford, England) · 2017Article
- A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.The annals of applied statistics · 2014Article
- Detection of candidate tumor driver genes using a fully integrated Bayesian approach.Statistics in medicine · 2014Article
- A Bayesian extension of the hypergeometric test for functional enrichment analysis.Biometrics · 2014Article
- Bayesian correlated clustering to integrate multiple datasets.Bioinformatics (Oxford, England) · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We consider integrative modeling of multiple gene networks and diverse genomic data, including protein-DNA binding, gene expression and DNA sequence data, to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated together by a TF. A key contribution of our work is that, to maximize the use of all existing biological knowledge, we allow incorporation of multiple gene networks into joint modeling of genomic data by introducing a mixture model based on the use of multiple Markov random fields (MRFs). Another important contribution of our work is to allow different genomic data to be correlated and to examine the validity and effect of the independence assumption as adopted in existing methods. Due to a fully Bayesian approach, inference about model parameters can be carried out based on MCMC samples. Application to an E. coli data set, together with simulation studies, demonstrates the utility and statistical efficiency gains with the proposed joint model.
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Registered trials
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