title: Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data.
abstract: BACKGROUND
  Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance.
  
  
  RESULTS
  Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances.
  
  
  CONCLUSIONS
  The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/ .
authors.affiliation: State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.|School of Computer, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.
authors.forename: Wen
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
authors.forename: Yanlin
authors.initials: Y
authors.lastname: Chen
authors.affiliation: International School of software, Wuhan University, Wuhan, 430072, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.affiliation: State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.|School of Computer, Wuhan University, Wuhan, 430072, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Luo
authors.affiliation: State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.|School of Computer, Wuhan University, Wuhan, 430072, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Tian
authors.affiliation: State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.|School of Computer, Wuhan University, Wuhan, 430072, China.
authors.forename: Xiaohong
authors.initials: X
authors.lastname: Li
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical
keywords: Drug-drug interaction; Ensemble learning; Missing link prediction; Random walk