Diverse approaches to predicting drug-induced liver injury using gene-expression profiles
| Autor: | Emi Ford, G. Rex Sumsion, Jeremy T. Beales, Daniel J. Garrett, Ifeanyichukwu O. Nwosu, Stephen R. Piccolo, Griffin R. G. Caryotakis, Michael Bradshaw, Emily D. LeBaron |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Drug
Normalization (statistics) media_common.quotation_subject Immunology Drug development Computational biology Biology Models Biological Risk Assessment General Biochemistry Genetics and Molecular Biology 03 medical and health sciences Drug withdrawal 0302 clinical medicine Machine learning medicine Humans lcsh:QH301-705.5 Ecology Evolution Behavior and Systematics 030304 developmental biology media_common Hyperparameter 0303 health sciences Applied Mathematics Research Gene Expression Profiling Precision medicine medicine.disease Classification Statistical classification lcsh:Biology (General) 030220 oncology & carcinogenesis Modeling and Simulation Test set Cell lines Chemical and Drug Induced Liver Injury General Agricultural and Biological Sciences Transcriptome Algorithms |
| Zdroj: | Biology Direct Biology Direct, Vol 15, Iss 1, Pp 1-12 (2020) |
| ISSN: | 1745-6150 |
| Popis: | Background 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 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 a consistent basis with high accuracy. In an attempt to improve accuracy, we aggregated predictions for six of the algorithms (excluding one that had performed exceptionally poorly) using a soft-voting method. This approach also failed to generalize well to the test set. We investigated alternative approaches—including a multi-sample normalization method, dimensionality-reduction techniques, a class-weighting scheme, and expanding the number of hyperparameter combinations used as inputs to the soft-voting method. We met limited success with each of these solutions. Conclusions We conclude that alternative methods and/or datasets will be necessary to effectively predict DILI in patients based on RNA expression levels in cell lines. Reviewers This article was reviewed by Paweł P Labaj and Aleksandra Gruca (both nominated by David P Kreil). |
| Databáze: | OpenAIRE |
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