Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles.

Journal: Artificial intelligence in medicine
Published Date: Nov 1, 2017

Abstract

OBJECTIVE: Synergistic drug combinations are promising therapies for cancer treatment. However, effective prediction of synergistic drug combinations is quite challenging as mechanisms of drug synergism are still unclear. Various features such as drug response, and target networks may contribute to prediction of synergistic drug combinations. In this study, we aimed to construct a computational model to predict synergistic drug combinations.

Authors

  • Xiangyi Li
    Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, PR China; Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China.
  • Yingjie Xu
    Tongren Hospital Shanghai Jiao Tong University School of Medicine, 1111 Xianxia Road, Shanghai 200336, PR China.
  • Hui Cui
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, PR China.
  • Tao Huang
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Disong Wang
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China.
  • Baofeng Lian
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Guangrong Qin
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China. Electronic address: grqin@scbit.org.
  • Lanming Chen
    Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai 201306, PR China. Electronic address: lmchen@shou.edu.cn.
  • Lu Xie
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China. Electronic address: xielu@scbit.org.

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