title: Predicting drug target interactions using meta-path-based semantic network analysis.
abstract: BACKGROUND
  In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.
  
  
  RESULTS
  Supervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction.
  
  
  CONCLUSIONS
  The proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time.
authors.affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA. gang.fu@nih.gov.
authors.forename: Gang
authors.initials: G
authors.lastname: Fu
authors.affiliation: School of Informatics & Computing, Indiana University, 107 S. Indiana Ave, Bloomington, IN, USA.|School of Information Management, Wuhan University, Wuchang, Wuhan, Hubei, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ding
authors.affiliation: School of Informatics & Computing, Indiana University, 107 S. Indiana Ave, Bloomington, IN, USA.
authors.forename: Abhik
authors.initials: A
authors.lastname: Seal
authors.affiliation: Department of Medicine, Stanford University, 450 Serra Mall, Stanford, CA, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Chen
authors.affiliation: College of Computer and Information Science, Northeastern University, 360 Huntington Avenue, Boston, MA, USA.
authors.forename: Yizhou
authors.initials: Y
authors.lastname: Sun
authors.affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.
authors.forename: Evan
authors.initials: E
authors.lastname: Bolton
mesh_terms: D057225:Data Mining; D016208:Databases, Factual; D015195:Drug Design; D000069550:Machine Learning; D008956:Models, Chemical; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D000069437:Pharmacological Phenomena; D011506:Proteins / Q000737:chemistry
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins
keywords: Link prediction; Machine learning; Meta-path topological feature; Random forest; Semantic network analysis