Anticancer drug synergy prediction in understudied tissues using transfer learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jan 15, 2021

Abstract

OBJECTIVE: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems.

Authors

  • Yejin Kim
    School of Biomedical Informatics, University of Texas Health, Science Center at Houston, Houston, TX, USA.
  • Shuyu Zheng
    Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
  • Jing Tang
    Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Wenjin Jim Zheng
    Center for Safe Artificial Intelligence for Healthcare, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Zhao Li
    Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China. lzjoey@gmail.com.
  • Xiaoqian Jiang
    School of Biomedical Informatics, University of Texas Health, Science Center at Houston, Houston, TX, USA.

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