New Paper "Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine"

Qiang Gu, Anup Kumar, Simon Bray, Allison Creason, Alireza Khanteymoori, Vahid Jalili, Björn Grüning and Jeremy Goecks from the Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, United States of America, The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, United States of America and Bioinformatics Group, Department of Computer Science, University of Freiburg, Germany, have published a new manuscript about Galaxy-ML toolkit containg machine and deep learning tools in Galaxy.

Abstract

Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.

The Galaxy-ML platform provides all the tools necessary to define a learner, train it, evaluate it, and visualize its performance. Panel B is a screenshot of the Galaxy tool to create a gradient boosted classifier. Panel C shows a Galaxy workflow to create a learner using a pipeline, perform hyperparameter search, and visualize the results.

Deep learning workflows:

All the machine learning and deep learning tools available in Galaxy-ML is available on https://usegalaxy.eu/ and there are several training materials on the GTN (https://training.galaxyproject.org/training-material/topics/statistics/) to start learning these tools. Happy using Machine Learning!

{% twitter https://twitter.com/bjoerngruening/status/1400051388441403392 %}