Predictability of drug-induced liver injury by machine learning
作者: Marco ChiericiMargherita FrancescattoNicole BussolaGiuseppe JurmanCesare Furlanello
作者单位: 1Fondazione Bruno Kessler, Via Sommarive 18, 38123, Trento, Italy
2Department CIBIO, University of Trento, Via Sommarive 9, 38123, Trento, Italy
刊名: Biology Direct, 2020, Vol.15 (1), pp.5-20
来源数据库: Springer Nature Journal
DOI: 10.1186/s13062-020-0259-4
关键词: Deep learningDILIClassificationMicroarrayCMap
英文摘要: Abstract(#br)Background(#br)Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction. Methods and results(#br)The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on...
全文获取路径: Springer Nature  (合作)
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影响因子:2.72 (2012)

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