Development of a prediction method for drug-induced liver injury by machine learning using large-scale adverse drug reaction data
-
- TAKADA Waki
- Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University
-
- AMBE Kaori
- Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University
-
- TOHKIN Masahiro
- Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University
Bibliographic Information
- Other Title
-
- 大規模副作用データを利用した機械学習による薬物性肝障害の予測手法の開発
Abstract
<p>In this study, we focused on developing a Drug-Induced Liver Injury (DILI) prediction method by machine learning from chemical structure information of drugs using large-scale adverse drug reaction data. We constructed the higher accurate prediction model by the random forest using the dataset combining the Japanese Adverse Drug Event Report (JADER) database and Drug Induced Liver Injury Rank (DILIrank), compared with using JADER alone. These results suggest that our model is useful to classify DILI-causative drugs with high accuracy using large-scale adverse drug reaction data.</p>
Journal
-
- Annual Meeting of the Japanese Society of Toxicology
-
Annual Meeting of the Japanese Society of Toxicology 47.1 (0), P-96S-, 2020
The Japanese Society of Toxicology
- Tweet
Details 詳細情報について
-
- CRID
- 1390848647545179648
-
- NII Article ID
- 130007898512
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- CiNii Articles
-
- Abstract License Flag
- Disallowed