The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications. Issue 4 (2nd June 2022)

• Record Type: Journal Article
• Authors: Chen, Zhao; Jiang, Yin; Zhang, Xiaoyu; Zheng, Rui; Qiu, Ruijin; Sun, Yang; Zhao, Chen; Shang, Hongcai
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 4(2022)
• Publication Date: 2022-06-02
• Subjects: ResNet18DNN -- drug-induced liver injury -- response to Letter to Editor -- artificial intelligence; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac196
• Languages: English
• ISSNs: 1467-5463

Abstract

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.