Abstract
The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research.
Item Type: | Paper |
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Keywords: | random forest, regression and classification trees, genetic association studies, variable importance, bias |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
Subjects: | 500 Science > 510 Mathematics |
URN: | urn:nbn:de:bvb:19-epub-13766-3 |
Language: | English |
Item ID: | 13766 |
Date Deposited: | 31. Jul 2012, 18:24 |
Last Modified: | 04. Nov 2020, 12:54 |