title: Pathway-based drug repositioning using causal inference.
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.
authors.forename: Jiao
authors.initials: J
authors.lastname: Li
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Lu
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003424:Crohn Disease / Q000188:drug therapy; D058492:Drug Repositioning / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D016013:Likelihood Functions; D015203:Reproducibility of Results