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iFad: an integrative factor analysis model for drug-pathway association inference†
Author(s) -
Haisu Ma,
Hongyu Zhao
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts285
Subject(s) - inference , computational biology , computer science , identification (biology) , drug discovery , bayesian inference , bayesian probability , data mining , mechanism (biology) , machine learning , artificial intelligence , bioinformatics , biology , philosophy , botany , epistemology
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-drug-pathway associations.

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