Name
Title | ||
|---|---|---|
Galaxy-Ml: An Accessible, Reproducible, And Scalable Machine Learning Toolkit For Biomedicine |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1371/journal.pcbi.1009014 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 17 | 6 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Gu | 1 | 1 | 0.69 |
| Anup Kumar | 2 | 0 | 0.34 |
| Simon Bray | 3 | 1 | 1.04 |
| Allison Creason | 4 | 0 | 0.34 |
| Ali Reza Khanteymoori | 5 | 5 | 3.11 |
| Vahid Jalili | 6 | 1 | 0.69 |
| Björn A. Grüning | 7 | 28 | 8.46 |
| Jeremy Goecks | 8 | 3 | 2.46 |