title: PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm.
abstract: Combinatorial therapy is a promising strategy for combating complex diseases by improving the efficacy and reducing the side effects. To facilitate the identification of drug combinations in pharmacology, we proposed a new computational model, termed PDC-SGB, to predict effective drug combinations by integrating biological, chemical and pharmacological information based on a stochastic gradient boosting algorithm. To begin with, a set of 352 golden positive samples were collected from the public drug combination database. Then, a set of 732 dimensional feature vector involving biological, chemical and pharmaceutical information was constructed for each drug combination to describe its properties. To avoid overfitting, the maximum relevance & minimum redundancy (mRMR) method was performed to extract useful ones by removing redundant subsets. Based on the selected features, the three different type of classification algorithms were employed to build the drug combination prediction models. Our results demonstrated that the model based on the stochastic gradient boosting algorithm yield out the best performance. Furthermore, it is indicated that the feature patterns of therapy had powerful ability to discriminate effective drug combinations from non-effective ones. By analyzing various features, it is shown that the enriched features occurred frequently in golden positive samples can help predict novel drug combinations.
authors.affiliation: State Key Laboratory of Microbial Metabolism, and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Xu
authors.affiliation: State Key Laboratory of Microbial Metabolism, and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: xiongyi@sjtu.edu.cn.
authors.forename: Yi
authors.initials: Y
authors.lastname: Xiong
authors.affiliation: State Key Laboratory of Microbial Metabolism, and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Dai
authors.affiliation: State Key Laboratory of Microbial Metabolism, and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Kotni Meena
authors.initials: KM
authors.lastname: Kumari
authors.affiliation: State Key Laboratory of Microbial Metabolism, and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Xu
authors.affiliation: State Key Laboratory of Microbial Metabolism, and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Hong-Yu
authors.initials: HY
authors.lastname: Ou
authors.affiliation: State Key Laboratory of Microbial Metabolism, and College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Dong-Qing
authors.initials: DQ
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D062313:Databases, Pharmaceutical; D004338:Drug Combinations; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions; D008962:Models, Theoretical; D013269:Stochastic Processes; D013678:Technology, Pharmaceutical / Q000379:methods
chemical_list: D004338:Drug Combinations
keywords: Drug combinations; Feature patterns; Feature selection; Stochastic gradient boosting