exprso: an R-package for the rapid implementation of machine learning algorithms

Quinn, Thomas, Tylee, Daniel and Glatt, Stephen 2017, exprso: an R-package for the rapid implementation of machine learning algorithms, F1000research, vol. 5, pp. 1-11, doi: 10.12688/f1000research.9893.2.

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Title exprso: an R-package for the rapid implementation of machine learning algorithms
Author(s) Quinn, Thomas
Tylee, Daniel
Glatt, Stephen
Journal name F1000research
Volume number 5
Article ID 2588
Start page 1
End page 11
Total pages 11
Publisher F1000 Research Ltd
Place of publication London, Eng.
Publication date 2017-12
ISSN 2046-1402
Keyword(s) R
classification
cross-validation
genomics
machine learning
package
prediction
supervised
unsupervised
Language eng
DOI 10.12688/f1000research.9893.2
Field of Research 080101 Adaptive Agents and Intelligent Robotics
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Quinn T et al.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30109423

Document type: Journal Article
Collections: School of Medicine
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