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- Title
Pathway-based drug repositioning using causal inference.
- Authors
Jiao Li; Zhiyong Lu
- Abstract
Background: Recent in vivo studies showed new hopes of drug repositioning through causality inference from drugs to disease. Inspired by their success, here we present an in silico method for building a causal network (CauseNet) between drugs and diseases, in an attempt to systematically identify new therapeutic uses of existing drugs. Methods: Unlike the traditional 'one drug-one target-one disease' causal model, we simultaneously consider all possible causal chains connecting drugs to diseases via target- and gene-involved pathways based on rich information in several expert-curated knowledge-bases. With statistical learning, our method estimates transition likelihood of each causal chain in the network based on known drug-disease treatment associations (e.g. bexarotene treats skin cancer). Results: To demonstrate its validity, our method showed high performance (AUC = 0.859) in cross validation. Moreover, our top scored prediction results are highly enriched in literature and clinical trials. As a showcase of its utility, we show several drugs for potential re-use in Crohn's Disease. Conclusions: We successfully developed a computational method for discovering new uses of existing drugs based on casual inference in a layered drug-target-pathway-gene- disease network. The results showed that our proposed method enables hypothesis generation from public accessible biological data for drug repositioning.
- Subjects
MARKET repositioning; CAUSAL models; DRUG efficacy; STATISTICAL learning; DRUG utilization
- Publication
BMC Bioinformatics, 2013, Vol 14, Issue Suppl 16, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/1471-2105-14-S16-S3