{"doi":"10.1186/1752-0509-5-86","title":"Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks","abstract":null,"journal":"BMC Systems Biology","year":2011,"id":589812,"datarank":0.5136612836758926,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.22177476131759552,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.22177476131759552,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":6,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1509072,"name":"Peter J Woolf","orcid":null,"position":1,"is_corresponding":false},{"id":1509071,"name":"Angel Alvarez","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks","abstract":"BACKGROUND: Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful. RESULTS: Here we present 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. The Partially Observed Bipartite Network (POBN) approach developed here is tested using E. coli expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for E. coli was integrated with 266 E. coli gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data. CONCLUSION: POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21619639","pmcid":"PMC3117734","openalex_id":"https://openalex.org/W2010571965","authors":[],"funders":[{"funder_name":"NIDA NIH HHS","grant_id":"U54-DA-021519","title":null},{"funder_name":"National Institutes of Health","grant_id":"3U54DA021519-04S1","title":"Nation Center- Integrative Biomedical Informatics(RMI)"}],"total_grants":2,"fwci":0.2433,"citation_percentile":0.54423518,"influential_citations":0,"citation_trend":[{"year":2012,"count":1},{"year":2013,"count":1},{"year":2015,"count":1},{"year":2016,"count":1},{"year":2017,"count":1},{"year":2025,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://bmcsystbiol.biomedcentral.com/counter/pdf/10.1186/1752-0509-5-86","host_type":"journal"},{"url":"https://bmcsystbiol.biomedcentral.com/counter/pdf/10.1186/1752-0509-5-86","host_type":"publisher"},{"url":"https://doi.org/10.1186/1752-0509-5-86","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/21619639","host_type":"repository"},{"url":"https://hdl.handle.net/2027.42/112960","host_type":"repository"},{"url":"http://doi.org/10.1186/1752-0509-5-86","host_type":"repository"},{"url":"https://doaj.org/article/4226b21d147b4a59a4b661d0c5e82d4b","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3117734","host_type":"repository"},{"url":"https://hdl.handle.net/2027.42/134539","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC3117734","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC3117734?pdf=render","host_type":"Europe_PMC"},{"url":"https://bmcsystbiol.biomedcentral.com/track/pdf/10.1186/1752-0509-5-86","host_type":""},{"url":"http://dx.doi.org/10.1186/1752-0509-5-86","host_type":""},{"url":"https://dx.doi.org/10.1186/1752-0509-5-86","host_type":""},{"url":"https://doi.org/https://doi.org/10.1186/1752-0509-5-86","host_type":""}],"fields_of_study":["Gene Regulatory Network Analysis","Bioinformatics and Genomic Networks","Genomics and Chromatin Dynamics","0301 basic medicine","0303 health sciences","03 medical and health sciences","Algorithms","Bayes Theorem","Computational Biology","DNA-Directed RNA Polymerases","Escherichia coli","Gene Expression Profiling","Gene Expression Regulation, Bacterial","Gene Regulatory Networks","Genes, Bacterial","Humans","Models, Biological","Models, Genetic","RNA, Messenger","Sigma Factor","Systems Biology"],"mesh_terms":["Algorithms","Bayes Theorem","Escherichia coli","Genes, Bacterial","Humans","Models, Biological","Models, Genetic","DNA-Directed RNA Polymerases","RNA, Messenger","Sigma Factor","Gene Expression Regulation, Bacterial","Computational Biology","Gene Expression Profiling","Systems Biology","Gene Regulatory Networks"],"keywords":["Gene regulatory network","Regulation of gene expression","Regulator","Computational biology","Gene expression","Biology","Transcriptional regulation","Systems biology","Gene","Regulatory sequence","Genetics","Models, Genetic","Applied Mathematics","Gene Expression Profiling","Bayes Theorem","Sigma Factor","DNA-Directed RNA Polymerases","Gene Expression Regulation, Bacterial","Models, Biological","Structural Biology","Genes, Bacterial","Modelling and Simulation","Escherichia coli","Humans","Gene Regulatory Networks","RNA, Messenger","Molecular Biology","Algorithms","Research Article"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"geo"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-24T08:37:11.017595Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}