SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.

Journal: BMC bioinformatics
Published Date: Sep 10, 2021

Abstract

BACKGROUND: One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model.

Authors

  • Zhaorui Zuo
    Institute of Medical Robotics, Shanghai Jiao Tong University, 2F of the Translational Medicine Building, No. 800 Dongchuan Road, Shanghai, 200000, China.
  • Penglei Wang
    State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, Guangzhou, China.
  • Xiaowei Chen
    School of Elderly Care Services and Management, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
  • Li Tian
    Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China. tianlixy3@csu.edu.cn.
  • Hui Ge
    Chinese Center for Disease Control and Prevention, 102206 Beijing, China.
  • Dahong Qian

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