title: A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).
abstract: BACKGROUND
  Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs.
  
  
  RESULTS
  In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper.
  
  
  CONCLUSIONS
  Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, China.
authors.forename: Qifan
authors.initials: Q
authors.lastname: Kuang
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, China.
authors.forename: MinQi
authors.initials: M
authors.lastname: Wang
authors.affiliation: College of Computer Science, Sichuan University, Chengdu, China.
authors.forename: Rong
authors.initials: R
authors.lastname: Li
authors.affiliation: College of Life Science, Sichuan University, Chengdu, China.
authors.forename: YongCheng
authors.initials: Y
authors.lastname: Dong
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, China.
authors.forename: Yizhou
authors.initials: Y
authors.lastname: Li
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, China.
authors.forename: Menglong
authors.initials: M
authors.lastname: Li
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D019540:Area Under Curve; D003198:Computer Simulation; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans