Keywords
Drug-induced liver injury, Gene-expression data, DILI risk prediction, Bioinformatics
Abstract
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.
Original Publication Citation
Sumsion GR†, Bradshaw III MS†, Beales JT†, Ford E†, Caryotakis GRG†, Garrett DJ†, LeBaron ED†, Nwosu IO‡, Piccolo SR*. Diverse approaches to predicting drug-induced liver injury using gene-expression profiles. Biology Direct, 2020, 15:1
BYU ScholarsArchive Citation
Sumsion, G. Rex; Bradshaw, Michael S. III; Beales, Jeremy T.; Ford, Emi; Caryotakis, Griffin R. G.; Garrett, Daniel J.; LeBaron, Emily D.; Nwosu, Ifeanyichukwu O.; and Piccolo, Stephen R., "Diverse Approaches to Predicting Drug-induced Liver Injury Using Gene-expression Profiles" (2020). Faculty Publications. 7504.
https://scholarsarchive.byu.edu/facpub/7504
Document Type
Peer-Reviewed Article
Publication Date
2020-01-15
Publisher
BioMed Central
Language
English
College
Life Sciences
Department
Biology
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