title: Drug-target interaction prediction by learning from local information and neighbors.
abstract: BACKGROUND
  In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug-target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known interactions available, its pure 'local' model is not able to be learned and hence BLM may fail to make correct prediction when involving such kind of new candidates.
  
  
  RESULTS
  We present a simple procedure called neighbor-based interaction-profile inferring (NII) and integrate it into the existing BLM method to handle the new candidate problem. Specifically, the inferred interaction profile is treated as label information and is used for model learning of new candidates. This functionality is particularly important in practice to find targets for new drug-candidate compounds and identify targeting drugs for new target-candidate proteins. Consistent good performance of the new BLM-NII approach has been observed in the experiment for the prediction of interactions between drugs and four categories of target proteins. Especially for nuclear receptors, BLM-NII achieves the most significant improvement as this dataset contains many drugs/targets with no interactions in the cross-validation. This demonstrates the effectiveness of the NII strategy and also shows the great potential of BLM-NII for prediction of compound-protein interactions.
  
  
  BACKGROUND
  jpmei@ntu.edu.sg
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798. jpmei@ntu.edu.sg
authors.forename: Jian-Ping
authors.initials: JP
authors.lastname: Mei
authors.forename: Chee-Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: Peng
authors.initials: P
authors.lastname: Yang
authors.forename: Xiao-Li
authors.initials: XL
authors.lastname: Li
authors.forename: Jie
authors.initials: J
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D055808:Drug Discovery; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000737:chemistry; D011506:Proteins / Q000737:chemistry / Q000187:drug effects*; D018160:Receptors, Cytoplasmic and Nuclear / Q000187:drug effects
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins; D018160:Receptors, Cytoplasmic and Nuclear