Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks
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Summary
POBN is presented, a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones.
- Type
- article
- Published
- 2011-05-27
- Cited by
- 7
- References
- 31
- Access
- Open access
- OpenAlex
- https://openalex.org/W2010571965
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1725782
Keywords
Gene regulatory network, Regulation of gene expression, Regulator, Computational biology, Gene expression
References
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- Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.
- Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data
- Inferring Cellular Networks Using Probabilistic Graphical Models
- RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation
- Markov Chain Monte Carlo in Practice
- Bayesian analysis of signaling networks governing embryonic stem cell fate decisions
- TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes
Cited by
- Comparing Statistical Methods for Constructing Large Scale Gene Networks
- Genome scale transcriptional response diversity among ten ecotypes of Arabidopsis thaliana during heat stress
- Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses
- A latent-observed dissimilarity measure
- Network growth models: A behavioural basis for attachment proportional to fitness
- Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning
- Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning
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