MSDRP: a deep learning model based on multisource data for predicting drug response.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines.

Authors

  • Haochen Zhao
    Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Xiaoyu Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Qichang Zhao
    School of Computer Science and Engineering, Central South University, China.
  • Yaohang Li
  • Jianxin Wang