A predictive toxicogenomics signature to classify genotoxic versus non-genotoxic chemicals in human TK6 cells
Why this work is in the frame
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Bibliographic record
Abstract
Genotoxicity testing is a critical component of chemical assessment. The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the DNA damage response pathways involved in response, is becoming more common. In companion papers previously published in Environmental and Molecular Mutagenesis, Li et al. (2015) [6] developed a dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in human lymphoblastoid TK6 cells in culture. This optimization approach was applied to the analysis of TK6 cells exposed to one of 14 genotoxic or 14 non-genotoxic agents, with sampling 4 h post-exposure. Microarray-based transcriptomic analyses were then used to develop a classifier for genotoxicity using the nearest shrunken centroids method. A panel of 65 genes was identified that could accurately classify toxicants as genotoxic or non-genotoxic. In Buick et al. (2015) [1], the utility of the biomarker for chemicals that require metabolic activation was evaluated. In this study, TK6 cells were exposed to increasing doses of four chemicals (two genotoxic that require metabolic activation and two non-genotoxic chemicals) in the presence of rat liver S9 to demonstrate that S9 does not impair the ability to classify genotoxicity using this genomic biomarker in TK6cells.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it