<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Alvarez A</submitter><funding>NIDA NIH HHS</funding><pagination>243</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC3128037</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>12</volume><pubmed_abstract>&lt;h4>Background&lt;/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.&lt;h4>Results&lt;/h4>The top regu</pubmed_abstract><journal>BMC bioinformatics</journal><pubmed_title>RegNetB: predicting relevant regulator-gene relationships in localized prostate tumor samples.</pubmed_title><pmcid>PMC3128037</pmcid><funding_grant_id>U54-DA-021519</funding_grant_id><pubmed_authors>Alvarez A</pubmed_authors><pubmed_authors>Woolf PJ</pubmed_authors></additional><is_claimable>false</is_claimable><name>RegNetB: predicting relevant regulator-gene relationships in localized prostate tumor samples.</name><description>&lt;h4>Background&lt;/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.&lt;h4>Results&lt;/h4>The top regu</description><dates><release>2011-01-01T00:00:00Z</release><publication>2011 Jun</publication><modification>2026-04-07T14:47:57.096Z</modification><creation>2026-04-07T14:18:30.274Z</creation></dates><accession>S-EPMC3128037</accession><cross_references><pubmed>21682879</pubmed><doi>10.1186/1471-2105-12-243</doi></cross_references></HashMap>