A Bayesian regression approach to the inference of regulatory networks from gene expression data

Rogers, S. and Girolami, M. (2005) A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics, 21, pp. 3131-3137. (doi:10.1093/bioinformatics/bti487)

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Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rogers, Dr Simon and Girolami, Prof Mark
Authors: Rogers, S., and Girolami, M.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Bioinformatics

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
396841Probabilistic Reconstruction of Signalling Pathways & Identification of Novel Transcription Factors Employing Heterogeneous Genome-Wide dataMark GirolamiMedical Research Council (MRC)G0401466Computing Science