xML-workFlow: an end-to-end explainable scikit-learn workflow for rapid biomedical experimentation
Biomedical machine learning modeling often suffers from high computational resource consumption, poor code reusability, and insufficient reproducibility and traceability. To address these challenges, we propose an end-to-end, interpretable, lightweight ML workflow that innovatively integrates scikit-learn, MLflow, and SHAP—enabling automated experiment tracking, model training, post-hoc interpretability analysis, and modular extensibility. Designed as a template-based framework, it supports seamless cross-project transfer, significantly improving modeling efficiency, result reproducibility, and team collaboration. The workflow is open-sourced and has been adopted by multiple bioinformatics teams for disease prediction and multi-omics analysis tasks. It establishes the first standardized, production-ready ML engineering practice tailored to biomedical research, bridging a critical gap between methodological innovation and scalable, transparent, and maintainable ML deployment in the domain.