Accessible, Reproducible, and Scalable Machine Learning for Biomedicine
Qiang Gu, Anup Kumar, Simon A. Bray, Allison Creason, Alireza Khanteymoori, Vahid Jalili, Björn Andreas Grüning, Jeremy Goecks · bioRxiv (Cold Spring Harbor Laboratory) · 2020
Abstract Supervised machine learning, where the goal is to predict labels of new instances by training on labeled data, has become an essential tool in biomedical data analysis. To make supervised machine learning more accessible to biomedical scientists, we have developed Galaxy-ML, a platform that enables scientists to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy, a biomedical computational workbench used by tens of thousands of scientists across the world, with a machine learning tool suite that supports end-to-end analysis.