title: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.
abstract: BACKGROUND
  Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.
  
  
  RESULTS
  Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.
  
  
  CONCLUSIONS
  We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.
authors.affiliation: Bioinformatics and Bioengineering Program, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA. zxia@tmhs.org
authors.forename: Zheng
authors.initials: Z
authors.lastname: Xia
authors.forename: Ling-Yun
authors.initials: LY
authors.lastname: Wu
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
authors.forename: Stephen T C
authors.initials: ST
authors.lastname: Wong
mesh_terms: D001185:Artificial Intelligence; D030541:Databases, Genetic; D055808:Drug Discovery; D004347:Drug Interactions; D015894:Genome, Human; D006801:Humans; D025941:Protein Interaction Mapping; D017421:Sequence Analysis; D049490:Systems Biology; D018511:Systems Integration