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gkmSVM: an R package for gapped-kmer SVM.
Mahmoud Ghandi1, Morteza Mohammad-Noori2, Narges Ghareghani3
1The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Bioinformatics (Oxford, England)
|May 7, 2016
Summary
A new R package, gkmSVM, accelerates training of gapped-kmer Support Vector Machine (SVM) classifiers for DNA and protein sequences. This tool offers faster kernel matrix computation, improving efficiency for sequence analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning
Background:
- Gapped-kmer Support Vector Machines (SVMs) are powerful tools for analyzing biological sequences.
- Previous implementations of gkmSVM algorithms had limitations in computational speed.
Purpose of the Study:
- To introduce a new R package for training gapped-kmer SVM classifiers.
- To present an improved algorithm for faster kernel matrix calculation.
Main Methods:
- Development of a new R package named gkmSVM.
- Implementation of an optimized algorithm for kernel matrix computation.
- Support for various sequence kernels including gkmSVM, kmer-SVM, mismatch kernel, and wildcard kernel.
Main Results:
- The new gkmSVM algorithm achieves a 2- to 5-fold speedup in run time compared to the original gkmSVM.
- The package is compatible with DNA and protein sequences.
- The R package is available on CRAN for multiple platforms.
Conclusions:
- The gkmSVM R package offers a significant improvement in computational efficiency for sequence classification tasks.
- This tool facilitates more rapid and effective analysis of biological sequence data using SVMs.
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