The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications.

Journal: Briefings in bioinformatics
Published Date: Jul 18, 2022

Abstract

In the previous study, we developed the generalized drug-induced liver injury (DILI) prediction model-ResNet18DNN to predict DILI based on multi-source combined DILI dataset and achieved better performance than that of previously published described DILI prediction models. Recently, we were honored to receive the invitation from the editor to response the Letter to Editor by Liu Zhichao, et al. We were glad that our research has attracted the attention of Liu's team and they has put forward their opinions on our research. In this response to Letter to the Editor, we will respond to these comments.

Authors

  • Zhao Chen
  • Yin Jiang
    Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
  • Xiaoyu Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Rui Zheng
    HKUST-DT System and Media Laboratory, Hong Kong University of Science and Technology, HongKong.
  • Ruijin Qiu
    Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  • Yang Sun
    Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Chen Zhao
    Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China.
  • Hongcai Shang
    Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China.

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