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Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine
- Source :
- PLoS Computational Biology, Vol 17, Iss 6, p e1009014 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.
- Subjects :
- 0301 basic medicine
Science and Technology Workforce
Computer science
Astronomical Sciences
Careers in Research
computer.software_genre
Trees
Machine Learning
0302 clinical medicine
Medicine and Health Sciences
Biology (General)
GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
ComputingMilieux_MISCELLANEOUS
Ecology
Suite
Eukaryota
Plants
Celestial Objects
Professions
Oncology
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Scalability
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
Workbench
Supervised Machine Learning
Research Article
Computer and Information Sciences
Science Policy
QH301-705.5
Decision tree
Machine learning
03 medical and health sciences
Cellular and Molecular Neuroscience
Deep Learning
Biomedical data
Artificial Intelligence
Genetics
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Biomedicine
Web browser
business.industry
Deep learning
Organisms
Biology and Life Sciences
Cancers and Neoplasms
Computational Biology
Reproducibility of Results
Galaxies
030104 developmental biology
People and Places
Scientists
Population Groupings
Artificial intelligence
business
computer
Software
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 17
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....3ac864b1013bcac02022f608822f2717