{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T12:35:13Z","timestamp":1767962113925,"version":"3.49.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"20","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: It is well recognized that the effects of drugs are far beyond targeting individual proteins, but rather influencing the complex interactions among many relevant biological pathways. Genome-wide expression profiling before and after drug treatment has become a powerful approach for capturing a global snapshot of cellular response to drugs, as well as to understand drugs\u2019 mechanism of action. Therefore, it is of great interest to analyze this type of transcriptomic profiling data for the identification of pathways responsive to different drugs. However, few computational tools exist for this task.<\/jats:p>\n               <jats:p>Results: We have developed FacPad, a Bayesian sparse factor model, for the inference of pathways responsive to drug treatments. This model represents biological pathways as latent factors and aims to describe the variation among drug-induced gene expression alternations in terms of a much smaller number of latent factors. We applied this model to the Connectivity Map data set (build 02) and demonstrated that FacPad is able to identify many drug\u2013pathway associations, some of which have been validated in the literature. Although this method was originally designed for the analysis of drug-induced transcriptional alternation data, it can be naturally applied to many other settings beyond polypharmacology.<\/jats:p>\n               <jats:p>Availability and implementation: The R package \u2018FacPad\u2019 is publically available at: http:\/\/cran.open-source-solution.org\/web\/packages\/FacPad\/<\/jats:p>\n               <jats:p>Contact: \u00a0hongyu.zhao@yale.edu<\/jats:p>\n               <jats:p>Supplementary Information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts502","type":"journal-article","created":{"date-parts":[[2012,8,25]],"date-time":"2012-08-25T02:27:06Z","timestamp":1345861626000},"page":"2662-2670","source":"Crossref","is-referenced-by-count":19,"title":["FacPad: Bayesian sparse factor modeling for the inference of pathways responsive to drug treatment"],"prefix":"10.1093","volume":"28","author":[{"given":"Haisu","family":"Ma","sequence":"first","affiliation":[{"name":"1 Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511 and 2Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06520, USA"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"1 Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511 and 2Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06520, USA"}]}],"member":"286","published-online":{"date-parts":[[2012,8,24]]},"reference":[{"key":"2023012513134939200_bts502-B1","doi-asserted-by":"crossref","first-page":"D504","DOI":"10.1093\/nar\/gkj126","article-title":"Pathguide: a Pathway Resource List","volume":"34","author":"Bader","year":"2006","journal-title":"Nucleic Acids Res."},{"key":"2023012513134939200_bts502-B2","first-page":"421","article-title":"Mometasone furoate decreases adhesion molecule expression in psoriasis","volume":"8","author":"Berti","year":"1998","journal-title":"Eur. J. Dermatol."},{"key":"2023012513134939200_bts502-B3","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1198\/004017008000000334","article-title":"Quality assessment for short oligonucleotide microarray data","volume":"50","author":"Brettschneider","year":"2008","journal-title":"Technometrics"},{"key":"2023012513134939200_bts502-B4","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1126\/science.1158140","article-title":"Drug target identification using side-effect similarity","volume":"321","author":"Campillos","year":"2008","journal-title":"Science"},{"key":"2023012513134939200_bts502-B5","doi-asserted-by":"crossref","first-page":"D742","DOI":"10.1093\/nar\/gkr1014","article-title":"The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway\/genome databases","volume":"40","author":"Caspi","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"2023012513134939200_bts502-B6","doi-asserted-by":"crossref","first-page":"D1067","DOI":"10.1093\/nar\/gkq813","article-title":"The Comparative Toxicogenomics Database: update 2011","volume":"39","author":"Davis","year":"2011","journal-title":"Nucleic Acids Res."},{"key":"2023012513134939200_bts502-B7","first-page":"233","article-title":"The relationship between precision\u2013recall and ROC curves","author":"Davis","year":"2006","journal-title":"ICML \u201806 Proc. 23rd Int. Conf. Machine Learn"},{"key":"2023012513134939200_bts502-B8","doi-asserted-by":"crossref","first-page":"e9603","DOI":"10.1371\/journal.pone.0009603","article-title":"Predicting drug-target interaction networks based on functional groups and biological features","volume":"5","author":"He","year":"2010","journal-title":"PLoS One"},{"key":"2023012513134939200_bts502-B9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/nar\/gkn923","article-title":"Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists","volume":"37","author":"Huang da","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"2023012513134939200_bts502-B10","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nprot.2008.211","article-title":"Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources","volume":"4","author":"Huang da","year":"2009","journal-title":"Nat. Protoc."},{"key":"2023012513134939200_bts502-B11","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/1471-2105-11-144","article-title":"Large-scale prediction of protein\u2013protein interactions from structures","volume":"11","author":"Hue","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023012513134939200_bts502-B12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1089\/cmb.2008.10TT","article-title":"Identifying network of drug mode of action by gene expression profiling","volume":"16","author":"Iorio","year":"2009","journal-title":"J. Comput. Biol."},{"key":"2023012513134939200_bts502-B13","doi-asserted-by":"crossref","first-page":"14621","DOI":"10.1073\/pnas.1000138107","article-title":"Discovery of drug mode of action and drug repositioning from transcriptional responses","volume":"107","author":"Iorio","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA."},{"key":"2023012513134939200_bts502-B14","doi-asserted-by":"crossref","first-page":"e1000925","DOI":"10.1371\/journal.pcbi.1000925","article-title":"Drug-induced regulation of target expression","volume":"6","author":"Iskar","year":"2010","journal-title":"PloS Comput. Biol."},{"key":"2023012513134939200_bts502-B15","doi-asserted-by":"crossref","first-page":"D109","DOI":"10.1093\/nar\/gkr988","article-title":"KEGG for integration and interpretation of large-scale molecular data sets","volume":"40","author":"Kanehisa","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"2023012513134939200_bts502-B16","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1038\/nrd1549","article-title":"Docking and scoring in virtual screening for drug discovery: methods and applications","volume":"3","author":"Kitchen","year":"2004","journal-title":"Nat. Rev. Drug Discov."},{"key":"2023012513134939200_bts502-B17","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-1-61779-400-1_2","article-title":"The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals","volume":"802","author":"Kotera","year":"2012","journal-title":"Methods Mol. Biol."},{"key":"2023012513134939200_bts502-B18","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/nrc2044","article-title":"The Connectivity Map: a new tool for biomedical research","volume":"7","author":"Lamb","year":"2007","journal-title":"Nat. Rev. Cancer"},{"key":"2023012513134939200_bts502-B20","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1126\/science.1132939","article-title":"The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease","volume":"313","author":"Lamb","year":"2006","journal-title":"Science"},{"key":"2023012513134939200_bts502-B21","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.pneurobio.2005.03.002","article-title":"Role of retinoid signalling in the adult brain","volume":"75","author":"Lane","year":"2005","journal-title":"Prog. Neurobiol."},{"key":"2023012513134939200_bts502-B22","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1093\/hmg\/9.9.1259","article-title":"Decreased expression of striatal signaling genes in a mouse model of Huntington\u2019s disease","volume":"9","author":"Luthi-Carter","year":"2000","journal-title":"Hum. Mol. Genet."},{"key":"2023012513134939200_bts502-B23","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1093\/bioinformatics\/bts285","article-title":"iFad: an integrative factor analysis model for drug-pathway association inference","volume":"28","author":"Ma","year":"2012","journal-title":"Bioinformatics"},{"key":"2023012513134939200_bts502-B24","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/ng1180","article-title":"PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes","volume":"34","author":"Mootha","year":"2003","journal-title":"Nat. Genet."},{"key":"2023012513134939200_bts502-B25","doi-asserted-by":"crossref","first-page":"e1000397","DOI":"10.1371\/journal.pcbi.1000397","article-title":"Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening","volume":"5","author":"Nagamine","year":"2009","journal-title":"PLoS Comput. Biol."},{"key":"2023012513134939200_bts502-B26","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1038\/sj.npp.1300998","article-title":"Chronic administration of 13-cis-retinoic acid increases depression-related behavior in mice","volume":"31","author":"O\u2019Reilly","year":"2006","journal-title":"Neuropsychopharmacology"},{"key":"2023012513134939200_bts502-B27","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/1471-2105-8-61","article-title":"Factor analysis for gene regulatory networks and transcription factor activity profiles","volume":"8","author":"Pournara","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023012513134939200_bts502-B28","first-page":"012002","article-title":"Inference algorithms and learning theory for Bayesian sparse factor analysis","volume":"197","author":"Rattray","year":"2009","journal-title":"J. Phys.: Conf. Ser."},{"key":"2023012513134939200_bts502-B29","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1093\/bioinformatics\/btk017","article-title":"Bayesian sparse hidden components analysis for transcription regulation networks","volume":"22","author":"Sabatti","year":"2006","journal-title":"Bioinformatics"},{"key":"2023012513134939200_bts502-B30","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1038\/nrc1951","article-title":"The NCI60 human tumour cell line anticancer drug screen","volume":"6","author":"Shoemaker","year":"2006","journal-title":"Nat. Rev. Cancer"},{"key":"2023012513134939200_bts502-B31","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.tox.2009.09.014","article-title":"Application of connectivity mapping in predictive toxicology based on gene-expression similarity","volume":"268","author":"Smalley","year":"2010","journal-title":"Toxicology"},{"key":"2023012513134939200_bts502-B32","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA."},{"key":"2023012513134939200_bts502-B33","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ejphar.2005.08.058","article-title":"Effects of mometasone furoate on a rat allergic rhinitis model","volume":"524","author":"Tsumuro","year":"2005","journal-title":"Eur. J. Pharmacol."},{"key":"2023012513134939200_bts502-B34","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1021\/mp800102c","article-title":"Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors","volume":"6","author":"Vina","year":"2009","journal-title":"Mol. Pharm."},{"key":"2023012513134939200_bts502-B35","first-page":"733","article-title":"Bayesian factor regression models in the \u2018Large p, Small n\u2019 paradigm","volume":"7","author":"West","year":"2003","journal-title":"Bayesian Stat."},{"key":"2023012513134939200_bts502-B36","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1146\/annurev-pharmtox-010611-134630","article-title":"Novel computational approaches to polypharmacology as a means to define responses to individual drugs","volume":"52","author":"Xie","year":"2012","journal-title":"Annu. Rev. Pharmacol. Toxicol."},{"key":"2023012513134939200_bts502-B37","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug\u2013target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012513134939200_bts502-B38","doi-asserted-by":"crossref","first-page":"i246","DOI":"10.1093\/bioinformatics\/btq176","article-title":"Drug\u2013target interaction prediction from chemical, genomic and pharmacological data in an integrated framework","volume":"26","author":"Yamanishi","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012513134939200_bts502-B39","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/nchembio.2007.53","article-title":"Integrating high-content screening and ligand\u2013target prediction to identify mechanism of action","volume":"4","author":"Young","year":"2008","journal-title":"Nat. Chem. Biol."},{"key":"2023012513134939200_bts502-B40","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1146\/annurev-pharmtox-010611-134520","article-title":"Systems pharmacology: network analysis to identify multiscale mechanisms of drug action","volume":"52","author":"Zhao","year":"2012","journal-title":"Annu. Rev. Pharmacol. Toxicol."},{"key":"2023012513134939200_bts502-B41","doi-asserted-by":"crossref","first-page":"4511","DOI":"10.1073\/pnas.1000488107","article-title":"Solving the apparent diversity-accuracy dilemma of recommender systems","volume":"107","author":"Zhou","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA."}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/20\/2662\/48872512\/bioinformatics_28_20_2662.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/20\/2662\/48872512\/bioinformatics_28_20_2662.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T19:14:41Z","timestamp":1674674081000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/28\/20\/2662\/206373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,8,24]]},"references-count":40,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2012,10,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bts502","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2012,10,15]]},"published":{"date-parts":[[2012,8,24]]}}}