{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T12:31:24Z","timestamp":1767961884466,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Pathway-based drug discovery considers the therapeutic effects of compounds in the global physiological environment. This approach has been gaining popularity in recent years because the target pathways and mechanism of action for many compounds are still unknown, and there are also some unexpected off-target effects. Therefore, the inference of drug-pathway associations is a crucial step to fully realize the potential of system-based pharmacological research. Transcriptome data offer valuable information on drug-pathway targets because the pathway activities may be reflected through gene expression levels. Hence, it is of great interest to jointly analyze the drug sensitivity and gene expression data from the same set of samples to investigate the gene-pathway\u2013drug-pathway associations.<\/jats:p>\n               <jats:p>Results: We have developed iFad, a Bayesian sparse factor analysis model to jointly analyze the paired gene expression and drug sensitivity datasets measured across the same panel of samples. The model enables direct incorporation of prior knowledge regarding gene-pathway and\/or drug-pathway associations to aid the discovery of new association relationships. We use a collapsed Gibbs sampling algorithm for inference. Satisfactory performance of the proposed model was found for both simulated datasets and real data collected on the NCI-60 cell lines. Our results suggest that iFad is a promising approach for the identification of drug targets. This model also provides a general statistical framework for pathway-based integrative analysis of other types of -omics data.<\/jats:p>\n               <jats:p>Availability: The R package \u2018iFad\u2019 and real NCI-60 dataset used are available at http:\/\/bioinformatics.med.yale.edu\/group\/.<\/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\/bts285","type":"journal-article","created":{"date-parts":[[2012,5,12]],"date-time":"2012-05-12T01:02:48Z","timestamp":1336784568000},"page":"1911-1918","source":"Crossref","is-referenced-by-count":26,"title":["iFad: an integrative factor analysis model for drug-pathway association inference\u2020"],"prefix":"10.1093","volume":"28","author":[{"given":"Haisu","family":"Ma","sequence":"first","affiliation":[{"name":"1 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511 and 2Division of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"1 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511 and 2Division of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA"}]}],"member":"286","published-online":{"date-parts":[[2012,5,10]]},"reference":[{"key":"2023012512432350100_B1","doi-asserted-by":"crossref","first-page":"D960","DOI":"10.1093\/nar\/gkq910","article-title":"CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge","volume":"39","author":"Ahmed","year":"2011","journal-title":"Nucleic Acids Res."},{"key":"2023012512432350100_B2","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":"2023012512432350100_B3","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.jmb.2009.09.049","article-title":"Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site","volume":"394","author":"Boyce","year":"2009","journal-title":"J. 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