xML-workFlow: an end-to-end explainable scikit-learn workflow for rapid biomedical experimentation
This addresses scalability and reproducibility challenges for bioinformaticians and biomedical researchers, but it is incremental as it combines existing tools into a template.
The paper tackled the resource-intensive process of building and iterating machine learning models in biomedical research by developing xML-workFlow, an end-to-end explainable workflow that integrates scikit-learn, MLflow, and SHAP, which significantly reduces time and effort for model development and iteration.
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.