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

Zhao Chen; Yin Jiang; Xiaoyu Zhang; Rui Zheng; Ruijin Qiu; Yang Sun; Chen Zhao; Hongcai Shang

doi:10.1093/bib/bbac196

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

Brief Bioinform. 2022 Jul 18;23(4):bbac196. doi: 10.1093/bib/bbac196.

Authors

Zhao Chen¹, Yin Jiang¹, Xiaoyu Zhang¹, Rui Zheng¹, Ruijin Qiu¹, Yang Sun¹, Chen Zhao², Hongcai Shang^{1

3}

Affiliations

¹ Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
² Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
³ College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China.

PMID: 35656709
DOI: 10.1093/bib/bbac196

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.

Keywords: ResNet18DNN; artificial intelligence; drug-induced liver injury; response to Letter to Editor.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Chemical and Drug Induced Liver Injury*
Humans