ArticleScientific reports2017
Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Multiomics analysis elucidated the role of inflammatory response and bile acid metabolism disturbance in electric shock-induced liver injury in mice.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2026Article
- ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery.Bioinformatics (Oxford, England) · 2025Article
- Incorporating Tissue-Specific Gene Expression Data to Improve Chemical-Disease Inference of in Silico Toxicogenomics Methods.Journal of xenobiotics · 2024Article
- Performance evaluation of E-nose and E-tongue combined with machine learning for qualitative and quantitative assessment of bear bile powder.Analytical and bioanalytical chemistry · 2023Article
- Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints.Journal of hazardous materials · 2022Article
- DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics.International journal of molecular sciences · 2021Article
- Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment.International journal of environmental research and public health · 2020Article
- Leveraging complementary computational models for prioritizing chemicals of developmental and reproductive toxicity concern: an example of food contact materials.Archives of toxicology · 2020Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation.
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