Diverse approaches to predicting drug-induced liver injury using gene-expression profiles
利用基因表达谱预测药物性肝损伤的多种方法
作者: G. Rex SumsionMichael S. BradshawJeremy T. BealesEmi FordGriffin R. G. CaryotakisDaniel J. GarrettEmily D. LeBaronIfeanyichukwu O. NwosuStephen R. Piccolo
作者单位: 1Department of Biology, Brigham Young University, Provo, UT, USA
刊名: Biology Direct, 2020, Vol.15 (1-3), pp.1310-1317
来源数据库: Springer Nature Journal
DOI: 10.1186/s13062-019-0257-6
关键词: Machine learningClassificationCell linesDrug developmentPrecision medicine
英文摘要: Abstract(#br)Background(#br)Drug-induced liver injury (DILI) is a serious concern during drug development and the treatment of human disease. The ability to accurately predict DILI risk could yield significant improvements in drug attrition rates during drug development, in drug withdrawal rates, and in treatment outcomes. In this paper, we outline our approach to predicting DILI risk using gene-expression data from Build 02 of the Connectivity Map (CMap) as part of the 2018 Critical Assessment of Massive Data Analysis CMap Drug Safety Challenge. Results(#br)First, we used seven classification algorithms independently to predict DILI based on gene-expression values for two cell lines. Similar to what other challenge participants observed, none of these algorithms predicted liver injury 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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