Healthcare
AutoML for Breast Cancer Variant Pathogenicity Prediction: Optimizing Dataset Design for Genomic AI
- machine learning
- artificial intelligence
- AutoML
- automated machine learning
- breast cancer
- genomic medicine
- pathogenicity prediction
- variant classification
- precision oncology
- clinical AI
- genomics
- SHAP interpretability
- dataset optimization
MLJAR tools were used in the following publication.
Leveraging AutoML to Optimize Dataset Selection for Improved Breast Cancer Variant Pathogenicity Prediction
Rahaf M. Ahmad, Noura Al Dhaheri, Mohd Saberi Mohamad, Bassam R. Ali
United Arab Emirates University, College of Medicine and Health Sciences | Multimedia University, Centre for Advanced Analytics and AI | Universitas Brawijaya
This study systematically benchmarks three leading AutoML frameworks—TPOT, H2O AutoML, and MLJAR—to determine the optimal dataset composition for breast cancer variant pathogenicity prediction. By evaluating four curated genomic datasets, the authors demonstrate that disease-specific, cancer-focused data significantly improves predictive performance, calibration, and model interpretability. The best-performing dataset achieved near-perfect AUC scores across frameworks, supported by SHAP, LIME, and permutation importance analyses highlighting biologically relevant conservation and ensemble-based features. The research establishes a scalable and clinically interpretable machine learning framework for precision oncology and genomic decision support systems.
Computational and Structural Biotechnology Journal • October 24, 2025
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