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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,818
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
affy—analysis of Affymetrix GeneChip data at the probe level
Gautier, L., Cope, L., Bolstad, B. M., & Irizarry, R. A. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg405 · Cited by 5,221
Li, Y., Yu, C. Y., Li, X. X., Zhang, P., Tang, J., Yang, Q., Fu, T., Zhang, X., Cui, X., Tu, G., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1076 · Cited by 529
Open TG-GATEs: a large-scale toxicogenomics database
Igarashi, Y., Nakatsu, N., Yamashita, T., Ono, A., Ohno, Y., Urushidani, T., & Yamada, H. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku955 · Cited by 465
Zou, Q., Zeng, J., Cao, L., & Ji, R. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.12.123 · Cited by 396
Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., & Li, X. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1415-9 · Cited by 357
NOREVA: normalization and evaluation of MS-based metabolomics data
Li, B., Tang, J., Yang, Q., Li, S., Cui, X., Li, Y., Chen, Y., Xue, W., Li, X., & Zhu, F. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx449 · Cited by 357
SVM-RFE With MRMR Filter for Gene Selection
Mundra, P. A., & Rajapakse, J. C. (2009) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2009.2035284 · Cited by 334
Cited by
Sorted by citations
Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
Zhang, H., Zou, Q., Ju, Y., Song, C., & Chen, D. (2022) · Current Bioinformatics · doi:10.2174/1574893617666220404145517 · Cited by 323
Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response
Su, R., Liu, X., Wei, L., & Zou, Q. (2019) · Methods · doi:10.1016/j.ymeth.2019.02.009 · Cited by 242
Self-citationClustering and classification methods for single-cell RNA-sequencing data
Qi, R., Ma, A., Ma, Q., & Zou, Q. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz062 · Cited by 218
Application of Machine Learning in Microbiology
Qu, K., Guo, F., Liu, X., Lin, Y., & Zou, Q. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00827 · Cited by 209
Self-citationACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides
Rao, B., Zhou, C., Zhang, G., Su, R., & Wei, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz088 · Cited by 178
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
