Mahmoud Ghandi1, Morteza Mohammad-Noori2, Narges Ghareghani3, Dongwon Lee4, Levi Garraway5, Michael A Beer6. 1. The Broad Institute of MIT and Harvard, Cambridge, MA, USA. 2. School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran. 3. Department of Engineering Science, College of Engineering, University of Tehran, and Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. 4. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA. 5. The Broad Institute of MIT and Harvard, Cambridge, MA, USA Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. 6. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Abstract
UNLABELLED: We present a new R package for training gapped-kmer SVM classifiers for DNA and protein sequences. We describe an improved algorithm for kernel matrix calculation that speeds run time by about 2 to 5-fold over our original gkmSVM algorithm. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel. AVAILABILITY AND IMPLEMENTATION: gkmSVM package is freely available through the Comprehensive R Archive Network (CRAN), for Linux, Mac OS and Windows platforms. The C ++ implementation is available at www.beerlab.org/gkmsvm CONTACT: mghandi@gmail.com or mbeer@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: We present a new R package for training gapped-kmer SVM classifiers for DNA and protein sequences. We describe an improved algorithm for kernel matrix calculation that speeds run time by about 2 to 5-fold over our original gkmSVM algorithm. This package supports several sequence kernels, including: gkmSVM, kmer-SVM, mismatch kernel and wildcard kernel. AVAILABILITY AND IMPLEMENTATION: gkmSVM package is freely available through the Comprehensive R Archive Network (CRAN), for Linux, Mac OS and Windows platforms. The C ++ implementation is available at www.beerlab.org/gkmsvm CONTACT: mghandi@gmail.com or mbeer@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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