xML-workFlow: an end-to-end explainable scikit-learn workflow for rapid biomedical experimentation

作     者：Tran, Khoa A. Pearson, John V. Waddell, Nicola 

作者机构：Medical Genomics Group Cancer Program QIMR Berghofer Medical Research Institute Brisbane Australia Genome Informatics Group Cancer Program QIMR Berghofer Medical Research Institute Brisbane Australia 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2025年

核心收录：

主　　题：XML 

摘      要：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. © 2025, CC BY-NC-SA.