A framework for categorizing sources of uncertainty in in silico toxicology methods: Considerations for chemical toxicity predictions
Bibliographic record
Abstract
Improving regulatory confidence and acceptance of in silico toxicology methods for chemical risk assessment requires assessment of associated uncertainties. Therefore, there is a need to identify and systematically categorize sources of uncertainty relevant to the methods and their predictions. In the present study, we analyzed studies that have characterized sources of uncertainty across commonly applied in silico toxicology methods. Our study reveals variations in the kind and number of uncertainty sources these studies cover. Additionally, the studies use different terminologies to describe similar sources of uncertainty; consequently, a majority of the sources considerably overlap. Building on an existing framework, we developed a new uncertainty categorization framework that systematically consolidates and categorizes the different uncertainty sources described in the analyzed studies. We then illustrate the importance of the developed framework through a case study involving QSAR prediction of the toxicity of five compounds, as well as compare it with the QSAR Assessment Framework (QAF). The framework can provide a structured (and potentially more transparent) understanding of where the uncertainties reside within in silico toxicology models and model predictions, thus promoting critical reflection on appropriate strategies to address the uncertainties. • Uncertainty communication is necessary for evaluating the robustness and validity of in silico toxicology methods. • A framework was developed to categorize uncertainty sources relevant to in silico toxicology methods. • Application of the framework was illustrated through chemical toxicity prediction using a QSAR model. • The relevance of the framework was discussed within the context of the OECD's (Q)SAR Assessment Framework.
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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 | 0.049 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.022 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".