{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Alvarez A"],"funding":["NIDA NIH HHS"],"pagination":["243"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3128037"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12"],"pubmed_abstract":["<h4>Background</h4>A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression.<h4>Results</h4>The top regu"],"journal":["BMC bioinformatics"],"pubmed_title":["RegNetB: predicting relevant regulator-gene relationships in localized prostate tumor samples."],"pmcid":["PMC3128037"],"funding_grant_id":["U54-DA-021519"],"pubmed_authors":["Alvarez A","Woolf PJ"],"additional_accession":[]},"is_claimable":false,"name":"RegNetB: predicting relevant regulator-gene relationships in localized prostate tumor samples.","description":"<h4>Background</h4>A central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression.<h4>Results</h4>The top regu","dates":{"release":"2011-01-01T00:00:00Z","publication":"2011 Jun","modification":"2026-04-07T14:47:57.096Z","creation":"2026-04-07T14:18:30.274Z"},"accession":"S-EPMC3128037","cross_references":{"pubmed":["21682879"],"doi":["10.1186/1471-2105-12-243"]}}