ArticleNucleic acids research2016
Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Hypothesis-driven approach to developmental toxicity assessment: Using mechanistic information to inform testing.Reproductive toxicology (Elmsford, N.Y.) · 2026Review
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- Knowledge graph aids comprehensive explanation of drug and chemical toxicity.CPT: pharmacometrics & systems pharmacology · 2023Article
- iGEM as a human iPS cell-based global epigenetic modulation detection assay provides throughput characterization of chemicals affecting DNA methylation.Scientific reports · 2023Article
- Knowledge-guided deep learning models of drug toxicity improve interpretation.Patterns (New York, N.Y.) · 2022Article
- StemPanTox: A fast and wide-target drug assessment system for tailor-made safety evaluations using personalized iPS cells.iScience · 2022Article
- Thalidomide affects limb formation and multiple myeloma related genes in human induced pluripotent stem cells and their mesoderm differentiation.Biochemistry and biophysics reports · 2021Article
- Oxidative Stress as a Common Key Event in Developmental Neurotoxicity.Oxidative medicine and cellular longevity · 2021Review
- QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors.Molecules (Basel, Switzerland) · 2020Article
- Early Transcriptomic Changes upon Thalidomide Exposure Influence the Later Neuronal Development in Human Embryonic Stem Cell-Derived Spheres.International journal of molecular sciences · 2020Article
- QSAR without borders.Chemical Society reviews · 2020Review
- Article
- Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.International journal of molecular sciences · 2018Review
- Prospects and Frontiers of Stem Cell Toxicology.Stem cells and development · 2017Review
- 17β-Oestradiol promotes differentiation of human embryonic stem cells into dopamine neurons via cross-talk between insulin-like growth factors-1 and oestrogen receptor β.Journal of cellular and molecular medicine · 2017Article
- Dose and Time Dependencies in Stress Pathway Responses during Chemical Exposure: Novel Insights from Gene Regulatory Networks.Frontiers in genetics · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5-100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development.
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