title: A phenome-guided drug repositioning through a latent variable model.
abstract: BACKGROUND
  The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution.
  
  
  RESULTS
  We developed our model on the phenome information in Side Effect Resource (SIDER). We first developed a LDA model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to "unknown" one at the time and then recovered based on the remaining drug-phenotype pairs. Of the probabilistically significant pairs, 70% was successfully recovered. Next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. We were able to retrieve approved indications of 6 drugs whose indications were not listed in SIDER. For 908 drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. Several of the suggested new uses can be supported with information from the scientific literature.
  
  
  CONCLUSIONS
  The results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. In this regard, LDA serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space.
authors.forename: Halil
authors.initials: H
authors.lastname: Bisgin
authors.forename: Zhichao
authors.initials: Z
authors.lastname: Liu
authors.forename: Hong
authors.initials: H
authors.lastname: Fang
authors.forename: Reagan
authors.initials: R
authors.lastname: Kelly
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. xwxu@ualr.edu.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Xu
authors.forename: Weida
authors.initials: W
authors.lastname: Tong
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D062313:Databases, Pharmaceutical; D058492:Drug Repositioning / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D015233:Models, Statistical; D010641:Phenotype
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