@article{QG-AK-SB-AC-AKh-VJ-BG-JG-Galaxy-ML-2021,
author = {Gu, Qiang and Kumar, Anup and Bray, Simon and Creason, Allison and Khanteymoori, Alireza and Jalili, Vahid and  Grüning, Björn and Goecks, Jeremy},
title = {Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine},
journal = {PLOS Computational Biology},
year = {2021},
doi = {10.1371/journal.pcbi.1009014},
volume = {17},
user = {kumara},
pages = {1-11},
number = {6},
month = {06},
issn = {1553-7358},
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.}
}