AI Article Synopsis
- A new R package for training gapped-kmer SVM classifiers has been developed, featuring a faster algorithm for kernel matrix calculations that improves run time by 2 to 5 times compared to the previous version.
- The package supports various sequence kernels, including gkmSVM, kmer-SVM, mismatch kernel, and wildcard kernel.
- It is freely available on CRAN for multiple operating systems, with further resources accessible at www.beerlab.org/gkmsvm and additional data available online.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937197 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btw203 | DOI Listing |
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Article Synopsis
- A new R package for training gapped-kmer SVM classifiers has been developed, featuring a faster algorithm for kernel matrix calculations that improves run time by 2 to 5 times compared to the previous version.
- The package supports various sequence kernels, including gkmSVM, kmer-SVM, mismatch kernel, and wildcard kernel.
- It is freely available on CRAN for multiple operating systems, with further resources accessible at www.beerlab.org/gkmsvm and additional data available online.
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