title: Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.
abstract: BACKGROUND
  During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.
  
  
  METHODS
  We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.
  
  
  RESULTS
  We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.
  
  
  CONCLUSIONS
  We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.
authors.forename: Yu-Fen
authors.initials: YF
authors.lastname: Huang
authors.forename: Hsiang-Yuan
authors.initials: HY
authors.lastname: Yeh
authors.forename: Von-Wun
authors.initials: VW
authors.lastname: Soo
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D015179:Colorectal Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery / Q000379:methods* / Q000706:statistics & numerical data; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects / Q000235:genetics; D053263:Gene Regulatory Networks / Q000187:drug effects / Q000235:genetics; D056726:Genetic Association Studies / Q000379:methods / Q000706:statistics & numerical data; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008297:Male; D058990:Molecular Targeted Therapy / Q000379:methods; D010597:Pharmacogenetics / Q000379:methods* / Q000706:statistics & numerical data; D010641:Phenotype; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D015398:Signal Transduction / Q000187:drug effects / Q000235:genetics; D059467:Transcriptome / Q000187:drug effects / Q000235:genetics
chemical_list: D000970:Antineoplastic Agents