title: A co-module approach for elucidating drug-disease associations and revealing their molecular basis.
abstract: BACKGROUND
  Understanding how drugs and diseases are associated in the molecular level is of critical importance to unveil disease mechanisms and treatments. Until recently, few studies attempt end to discover important gene modules shared by both drugs and diseases.
  
  
  RESULTS
  Here, we propose a novel presentation of drug-gene-disease relationship, a 'co-module', which is characterized by closely related drugs, diseases and genes. We first define a network-based gene closeness profile to relate drug to disease. Then, we develop a Bayesian partition method to identify drug-gene-disease co-modules underlying the gene closeness data. Genes share similar notable patterns with respect not only to the drugs but also the diseases within a co-module. Simulations show that our method, comCIPHER, achieves a better performance compared with a popular co-module detection method, PPA. We apply comCIPHER to a set consisting of 723 drugs, 275 diseases and 1442 genes and demonstrate that our co-module approach is able to identify new drug-disease associations and highlight their molecular basis. Disease co-morbidity emerges as well. Three co-modules are further illustrated in which new drug applications, including the anti-cancer metastasis activity of an anti-asthma drug Pranlukast, and a cardiovascular stress-testing agent Arbutamine for obesity, as well as potential side-effects, e.g. hypotension for Triamterene, are computationally identified.
  
  
  BACKGROUND
  The compiled version of comCIPHER can be found at http://bioinfo.au.tsinghua.edu.cn/comCIPHER/. The 86 co-modules can be downloaded from http://bioinfo.au.tsinghua.edu.cn/comCIPHER/Co_Module_Results.zip.
  
  
  BACKGROUND
  shaoli@mail.tsinghua.edu.cn
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China.
authors.forename: Shiwen
authors.initials: S
authors.lastname: Zhao
authors.forename: Shao
authors.initials: S
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D004198:Disease Susceptibility / Q000628:therapy*; D004358:Drug Therapy; D053263:Gene Regulatory Networks / Q000187:drug effects*; D006801:Humans; D008390:Markov Chains; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics; D012984:Software