title: Exploration and analysis of drug modes of action through feature integration.
abstract: Identifying drug modes of action (MoA) is of paramount importance for having a good grasp of drug indications in clinical tests. Anticipating MoA can help to discover new uses for approved drugs. Here we first used a drug-set enrichment analysis method to discover significant biological activities in every mode of action category. Then, we proposed a new computational model, a probability ensemble approach based on Bayesian network theory, which integrated chemical, therapeutic, genomic and phenotypic properties of over a thousand of FDA approved drugs to assist with the prediction of MoA. 10-fold cross validation tests demonstrate that this method can achieve better performances than four other methods with the area under the receiver operating characteristic (ROC) curves. Finally, we further conducted a large-scale prediction for drug-MoA pairs. Using the Cardiovascular Agents category as an example, several predicted drug-MoA pairs were supported by literature resources.
authors.affiliation: Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China. myliu@bio.ecnu.edu.cn.
authors.forename: Mingyuan
authors.initials: M
authors.lastname: Xin
authors.affiliation: Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China. zrjiang@cs.ecnu.edu.cn.
authors.forename: Jun
authors.initials: J
authors.lastname: Fan
authors.affiliation: Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China. myliu@bio.ecnu.edu.cn.
authors.forename: Mingyao
authors.initials: M
authors.lastname: Liu
authors.affiliation: Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China. zrjiang@cs.ecnu.edu.cn.
authors.forename: Zhenran
authors.identifier: http://orcid.org/0000-0002-5275-3632
authors.initials: Z
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D058492:Drug Repositioning; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D012372:ROC Curve; D015203:Reproducibility of Results