Where will genetic toxicology testing be in 30 years’ time? Summary report of the 25th Industrial Genotoxicity Group Meeting, Royal Society of Medicine, London, November 9, 2011
Bibliographic record
Abstract
A number of influences including legislation, industry and academia have encouraged advances in computational toxicology and high-throughput testing to probe more broadly putative toxicity pathways. The aim of the 25th United Kingdom Mutagen Society (UKEMS) Industrial Genotoxicity Group Annual Meeting 2011 was to explore current and upcoming research tools that may provide new cancer risk estimation approaches and discuss the genotoxicity testing paradigm of the future. The meeting considered whether computer modelling, molecular biology systems and/or adverse outcome pathway approaches can provide more accurate toxicity predictions and whether high-content study data, pluripotent stem cells or new scientific disciplines, such as epigenetics and adductomics, could be integrated into the risk assessment process. With close collaboration between industry, academia and regulators next generation predictive models and high-content tools have the potential to transform genetic toxicology testing in the 21st century.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".