New version is available for downloading now at the end of this page.

Methodology Description:
   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.
   Detailed description of the methodology and procedures can be found in:
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
 

Software Package:
    This package includes the binary files for R-SVM with the cross validation and permutation test procedures described in the technical report.  The package was compiled to run under Red Hat Linux release 6.2.  Some other versions will be available later at this same website.  Before that, for users of other systems, please contact Xuegong Zhang to check the availability.
    See the files README.txt, USAGE.txt and EXAMPLE.txt coming with the package for general instructions, usage instructions and examples.
    I'd like to remind you that if you want to use R-SVM, you'd better feel comfortable with using Linux/UNIX systems, although you don't need to be an expert at all. The reason that we do not use the more popular platforms is that some of the computations might take hours, days or even longer time, depending on your specific computer and data, it is more convenient (and safer!) to run such big jobs on UNIX/Linux environment. Sorry if this causes extra effort on your side.
    Whoever downloads this package is regarded as a "user" of R-SVM.  By downloading this package, the user agrees that the downloaded package will be used for academic use only, and agrees that any work based on or directly related to the download software and/or its documentations will cite the original work by Xuegong Zhang and Wing H. Wong (see the readme.txt file for details).  We should also be informed for any such publications.  For potential commercial users, please contact Xuegong Zhang or Wing H. Wong before using it on real data.
    Anyone who downloads this package is not supposed to circulate it without the agreement of the authors.  Please refer to this website for anyone who is interested in obtaining the package.
    The package is provided as is.  We are working hard to make it correct and in good shape, but we hereby disclaim all warranties with regard to this program, including merchantability and fitness.  Users are obligated to report any bugs in the package, either technical or methodological.
    This package utilizes the SVMTorch package (Ronan Collobert and Samy Bengio, SVMTorch: support vector machines for large-scale regression problems, Journal of Machine Learning Research, vol.1, pp.143-160, 2001 http://www.idiap.ch/learning/SVMTorch.html).  Users of R-SVM are required to take the responsibility to follow the original license agreement of SVMTorch.  (The license file of SVMTorch also comes with the R-SVM package).

Note on 12/31/05: This is the new version written in R by Xin Lu of HSPH.. Please report any bugs or inconveniences.