R-SVM 2.0 – Recursive Sample Classification and Gene Selection with SVM

R-SVM 2.0


R-SVM is a SVM-based method for doing supervised pattern recognition(classification) with microarray gene expression data.  The method uses SVM for both classification and for selecting a subset of relevant genes according to their relative contribution in the classification.  This process is done recursively so that a series of gene subsets and classification models can be obtained in a recursive manner, at different levels of gene selection.  The performance of the classification can be evaluated either on an independent test data set or by cross validation on the same data set.  R-SVM also includes an option for permutation experiments to assess the  significance of the performance.



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ZHANG, X.G., LU, X., (Joint First Author) XU, X.Q., LEUNG, H.E., WONG, W.H. and LIU, J.S. (2006)
RSVM: A SVM based Strategy for Recursive Feature Selection and Sample Classification with Proteomics Mass-Spectrometry Data.
BMC Bioinformatics, 7:197

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