Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

Drug-induced hepatotoxicity may cause acute and chronic liver disease, leading to great concern for patient safety. It is also one of the main reasons for drug withdrawal from the market. Toxicogenomics data has been widely used in hepatotoxicity prediction. In our study, we proposed a multi-dose co...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2019-07, Vol.16 (4), p.1231-1239
Main Authors: Su, Ran, Wu, Huichen, Xu, Bo, Liu, Xiaofeng, Wei, Leyi
Format: Article
Language:English
Subjects:
Algorithms
Biological system modeling
Chemical and Drug Induced Liver Injury
Computational Biology - instrumentation
Computational Biology - methods
computational model
Computational modeling
Computer applications
Computer Simulation
Data models
Databases, Factual
Drug dosages
Drug-induced
Drug-Related Side Effects and Adverse Reactions
Drugs
Feature extraction
Gene Expression
Hepatotoxicity
Humans
Liver - drug effects
Liver diseases
Models, Statistical
multi-dose
Oligonucleotide Array Sequence Analysis
prediction
Prediction models
Predictive models
Recording
Reproducibility of Results
Support Vector Machine
Support vector machines
Toxicity
Toxicogenetics - statistics & numerical data
toxicogenomics
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Summary:Drug-induced hepatotoxicity may cause acute and chronic liver disease, leading to great concern for patient safety. It is also one of the main reasons for drug withdrawal from the market. Toxicogenomics data has been widely used in hepatotoxicity prediction. In our study, we proposed a multi-dose computational model to predict the drug-induced hepatotoxicity based on gene expression and toxicity data. The dose/concentration information after drug treatment is fully utilized in our study based on the dose-response curve, thus a more informative representative of the dose-response relationship is considered. We also proposed a new feature selection method, named MEMO, which is also one important aspect of our multi-dose model in our study, to deal with the high-dimensional toxicogenomics data. We validated the proposed model using the TG-GATEs, which is a large database recording toxicogenomics data from multiple views. The experimental results show that the drug-induced hepatotoxicity can be predicted with high accuracy and efficiency using the proposed predictive model.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2018.2858756