The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data
Abstract
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.
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“The metrics are used in evaluating the performance frequently [64,65,66,67,68,69,70,71,72].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [67]
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Metadata
- Authors
- Su, R., Wu, H., Xu, B., Liu, X., & Wei, L.
- Affiliations
- Tianjin University (CN)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Venue
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Published
- 2019-07-01
- Volume
- 16
- Issue
- 4
- Pages
- 1231-1239
- ISSN
- 1545-5963 (ISSN-L)
- 1557-9964
- 2374-0043
- OpenAlex
- W2884581375
- Cited by
- 134
- References
- 67
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 6 · 2023: 9 · 2022: 19 · 2021: 39 · 2020: 28 · 2019: 24
- FWCI
- 26.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61701340, 61702361)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- drug-induced liver injury, hepatotoxicity prediction, toxicogenomics, dose-response relationship, gene expression, feature selection, MEMoS, TG-GATEs, high-dimensional data, drug safety
- MeSH terms
- Algorithms, Computer Simulation, Humans, Liver, Reproducibility of Results, Models, Statistical, Gene Expression, Databases, Factual, Computational Biology, Oligonucleotide Array Sequence Analysis, Toxicogenetics, Chemical and Drug Induced Liver Injury, Support Vector Machine, Drug-Related Side Effects and Adverse Reactions
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