title: A hadoop-based method to predict potential effective drug combination.
abstract: Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.
authors.affiliation: State Key Laboratory of Microbial Metabolism and College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Yifan
authors.identifier: 0000-0001-9738-6875
authors.initials: Y
authors.lastname: Sun
authors.affiliation: State Key Laboratory of Microbial Metabolism and College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Xiong
authors.affiliation: State Key Laboratory of Microbial Metabolism and College of Life Science 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 Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Dongqing
authors.initials: D
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D004338:Drug Combinations; D008962:Models, Theoretical
chemical_list: D004338:Drug Combinations