Consensus recommendations to promote and advance predictive systems toxicology and toxicogenomics
Bibliographic record
Abstract
The number of high throughput -omics technologies continues to grow. Toxicogenomic application of these technologies is poised to greatly influence current regulatory toxicology. However, many changes are needed before a systems biology level approach can be effectively incorporated into the regulatory toxicology framework. A workshop was held at the Annual Environmental Mutagen Society meeting in Vancouver, British Columbia, on advances in -omics applications. A number of recommendations emerged from the workshop discussion (beyond what activities are currently ongoing) aimed at advancing the ultimate goal of predictive systems toxicology from the present formative state of toxicogenomics. Recommendations include: (1) encouraging investigators to embrace open-access data sharing, (2) increasing current database and curation capacity, (3) establishment of large collaborative projects investigating multiple -omics endpoints within the same groups of animals, (4) mechanisms to encourage collaborative science including increasing the value of junior authorship on multi-authored papers and changes in the promotion process, (5) further development of standardized protocols, and (6) investment from the funding agencies and toxicology community to build the required infrastructure.
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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 | 0.132 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.016 | 0.011 |
| Research integrity | 0.030 | 0.030 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".