title: Predicting drug-target interaction networks of human diseases based on multiple feature information.
abstract: OBJECTIVE
  Drug-target interaction is crucial in the drug design process. Predicting the drug-target interaction networks of important human diseases can provide valuable clues for the characterization of the mechanism of action of diseases.
  
  
  METHODS
  A new graph-based semisupervised learning (GBSSL) method is proposed to predict the drug-target interaction networks involved in 13 types of diseases. According to the method, each drug-target pair is initially described with different biological features including sequence, structure, function and network topology information. Then, the optimal feature selection procedures based on the relief and minimum redundancy maximum relevance are executed, respectively. Finally, unknown drug-target interactions can be predicted by the GBSSL method effectively.
  
  
  RESULTS
  The proposed method can effectively predict drug-target interactions (with a receiver operating characteristic score of 94.8% and a precision-recall score of 76.5%).
  
  
  CONCLUSIONS
  Compared with the existing methods, the GBSSL method provides an efficient means of generating optimal features obtained from the combination of multiple sources of feature information.
authors.affiliation: Department of Computer Science & Technology, East China Normal University, 200241, Shanghai, China.
authors.forename: Weiming
authors.initials: W
authors.lastname: Yu
authors.forename: Yan
authors.initials: Y
authors.lastname: Yan
authors.forename: Qing
authors.initials: Q
authors.lastname: Liu
authors.forename: Junxiang
authors.initials: J
authors.lastname: Wang
authors.forename: Zhenran
authors.initials: Z
authors.lastname: Jiang
mesh_terms: D004338:Drug Combinations; D004358:Drug Therapy; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D058990:Molecular Targeted Therapy
chemical_list: D004338:Drug Combinations