An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings
<p>Abstract</p> <p>Background</p> <p>As computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (GWA) datasets becomes possible. While most traditional statistical methods can only eluci...
Main Authors: | Hubbard Alan E, Goldstein Benjamin A, Cutler Adele, Barcellos Lisa F |
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Format: | Article |
Language: | English |
Published: |
BMC
2010-06-01
|
Series: | BMC Genetics |
Online Access: | http://www.biomedcentral.com/1471-2156/11/49 |
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