Leveraging AutoML to optimize dataset selection for improved breast cancer variants pathogenicity prediction
Bibliographic record
Abstract
Breast cancer (BC) remains one of the most prevalent and lethal malignancies worldwide, with its onset shaped by complex interactions between germline predispositions, environmental exposures, and accumulated somatic mutations. Accurate prediction of variant pathogenicity is essential for identifying high-risk individuals, guiding early detection, and tailoring treatment strategies. However, existing computational tools often lack disease-specific training and fail to generalize across diverse variant datasets. To address this gap, we systematically benchmarked the predictive utility of four distinct variant datasets using three Automated Machine Learning (AutoML) frameworks-TPOT, H2O AutoML, and MLJAR. Our goal was to evaluate how dataset composition influences classification performance and to identify the optimal dataset for BC-specific pathogenicity prediction. Among the datasets evaluated, Dataset-2-curated from both cancer-specific and non-cancer databases, consistently yielded the highest predictive performance across all frameworks. H2O AutoML achieved a peak accuracy of 99.99 %, while TPOT and MLJAR also exhibited robust generalization on this dataset. Feature importance analyses revealed strong convergence across frameworks, highlighting conservation scores and pathogenicity metrics as dominant predictors. Interpretability techniques including SHAP, permutation importance, and LIME further validated the biological relevance and transparency of the models. This study presents a scalable, interpretable AutoML benchmarking framework tailored to the clinical prioritization of BC variants. By demonstrating the superiority of cancer-specific, disease-relevant datasets, our findings underscore the critical importance of thoughtful dataset design in machine learning pipelines for genomic medicine. Beyond BC, this framework is readily transferable to other genetic disorders, providing a foundational tool for precision diagnostics and the advancement of personalized oncology.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".