A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification |
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Authors: | Alexander Statnikov Lily Wang " target="_blank">Constantin F Aliferis |
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Institution: | (1) Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA;(2) Department of Biostatistics, Vanderbilt University, Nashville, TN, USA;(3) Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA;(4) Department of Computer Science, Vanderbilt University, Nashville, TN, USA |
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Abstract: | Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray
technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification
algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular
signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered
"best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers
may outperform support vector machines in this domain. |
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