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药物性肝损伤的预测方法:对人工智能在真实世界应用中可重复性科学问题的回应。

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

机构信息

Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.

Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.

出版信息

Brief Bioinform. 2022 Jul 18;23(4). 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.

摘要

在之前的研究中,我们开发了基于多源组合 DILI 数据集的广义药物性肝损伤 (DILI) 预测模型-ResNet18DNN,用于预测 DILI,并取得了优于先前发表的描述性 DILI 预测模型的性能。最近,我们很荣幸收到编辑的邀请,对刘智超等人的来信作出回应。我们很高兴我们的研究引起了刘团队的关注,他们对我们的研究提出了意见。在本次对来函的回应中,我们将对这些意见进行回复。

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