Literature DB >> 27153639

gkmSVM: an R package for gapped-kmer SVM.

Mahmoud Ghandi1, Morteza Mohammad-Noori2, Narges Ghareghani3, Dongwon Lee4, Levi Garraway5, Michael A Beer6.   

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.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2016        PMID: 27153639      PMCID: PMC4937197          DOI: 10.1093/bioinformatics/btw203

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


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5.  Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes.

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6.  A method to predict the impact of regulatory variants from DNA sequence.

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Journal:  Genome Res       Date:  2014-10-15       Impact factor: 9.043

8.  kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets.

Authors:  Christopher Fletez-Brant; Dongwon Lee; Andrew S McCallion; Michael A Beer
Journal:  Nucleic Acids Res       Date:  2013-06-14       Impact factor: 16.971

9.  Enhanced regulatory sequence prediction using gapped k-mer features.

Authors:  Mahmoud Ghandi; Dongwon Lee; Morteza Mohammad-Noori; Michael A Beer
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8.  Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

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9.  Computational identification of cell-specific variable regions in ChIP-seq data.

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10.  Comparative Analysis of Immune Cells Reveals a Conserved Regulatory Lexicon.

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