🤖 AI Summary
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.
📝 Abstract
Motivation: Building and iterating machine learning models is often a resource-intensive process. In biomedical research, scientific codebases can lack scalability and are not easily transferable to work beyond what they were intended. xML-workFlow addresses this issue by providing a rapid, robust, and traceable end-to-end workflow that can be adapted to any ML project with minimal code rewriting. Results: We show a practical, end-to-end workflow that integrates scikit-learn, MLflow, and SHAP. This template significantly reduces the time and effort required to build and iterate on ML models, addressing the common challenges of scalability and reproducibility in biomedical research. Adapting our template may save bioinformaticians time in development and enables biomedical researchers to deploy ML projects. Availability and implementation: xML-workFlow is available at https://github.com/MedicalGenomicsLab/xML-workFlow.