A Web-based software toolkit for accessible and best-practice machine learning analyses in biomedical research

Abstract

Machine learning is increasingly central to biomedical research, but using machine learning well often requires substantial computational expertise and methodological care to produce high-quality results. To make machine learning tools more accessible to biomedical researchers while supporting best-practice approaches, we developed the Galaxy Learning and Modeling (GLEAM) software toolkit. GLEAM enables researchers to perform supervised machine learning analyses through a set of web-based, code-free software tools for tabular, image, and multimodal biomedical datasets. GLEAM standardizes data partitioning, model selection, training, evaluation, and reporting, helping researchers apply machine learning with greater rigor and consistency. GLEAM runs on the Galaxy computational workbench and uses Galaxys core features to make all analyses accessible, reproducible, and scalable. We validated GLEAM on three biomedical tasks: predicting patient response to immunotherapy, skin lesion classification, and cancer recurrence prediction. Across these tasks, GLEAM produced highly accurate predictive models and improved transparency, reproducibility, and rigor.