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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".