Classification of Chemicals Based on Concentration-Dependent Toxicological Data Using ToxClust
Bibliographic record
Abstract
Concentration-dependent response relationships provide essential information on the characteristics of chemical-induced effects on toxicological end points, which include effect (inhibition or induction), potency, and efficacy of the chemical. Recent developments in systems biology and high throughputtechnologies have allowed simultaneous examination of many chemicals at multiple end point levels. While this increase in the quantity of information generated offers great potential, it also poses a significant challenge to environmental scientists to efficiently manage and interpret these large data sets. Here we present a novel method, ToxClust, that allows clustering of chemicals on the basis of concentration-response data derived with single or multiple end points. This method utilizes a least distance-searching algorithm (LDSA) to measure the pattern dissimilarity of concentration-response curves between chemicals and their relative toxic potency. ToxClust was tested using simulated data and chemical test data collected from the human H295R cell-based in vitro steroidogenesis assay. ToxClust effectively identified similar patterns of simulated data and responses to the exposure with the five model chemicals and separated them into different groups on the basis of their dissimilarities. These observations demonstrate that ToxClust not only provides an effective data analysis and visualization tool, but also has value in hypothesis generation and mechanism-based chemical classification.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".