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gkmSVM specifications

Information


Unique identifier OMICS_20453
Name gkmSVM
Alternative names gapped-kmer-SVM, gkmSVM-R
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input format BED, FORMAT
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++, R
License GNU General Public License version 3.0
Computer skills Advanced
Version 0.79.0
Stability Stable
Maintained Yes

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Documentation


Maintainers


  • person_outline Michael A. Beer <>
  • person_outline Mahmoud Ghandi <>

Additional information


https://cran.r-project.org/web/packages/gkmSVM/index.html A C++ code is also available http://www.beerlab.org/gkmsvm/downloads/gkmsvm-2.0.tar.gz.

Publications for gapped-kmer-SVM

gkmSVM in pipeline

2017
PMCID: 5870558
PMID: 28881989
DOI: 10.1093/bioinformatics/btx255

[…] similar k-mer features that are all significant for the prediction task (). to deal with such difficulties, seqgl () and mil () similarly adopt a dml method (homer) to interpret their outputs, while gkmsvm () would cluster k-mers into pwms for further analysis, which could be viewed as a simplified version of motif learning methods such as ()., there are several directions in which we intend […]


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gkmSVM in publications

 (9)
PMCID: 5954283
PMID: 29764360
DOI: 10.1186/s12864-018-4459-6

[…] 3d genomic interaction prediction methods fail to exploit sequence information except speid. at the meantime, there are many inspiring methods for 1d chromatin states prediction [, ], including gkmsvm for enhancer prediction [], deepsea for epigenomic state prediction [] and deepbind for dna/rna-binding proteins prediction [], which extract sequence features and yield high performance. […]

PMCID: 5856001
PMID: 29544533
DOI: 10.1186/s13059-018-1411-7

[…] in accuracy in the out-of-bag sample. to discriminate between dsb and non-dsb sites, we randomly selected genomic sequences that matched sizes, gc, and repeat contents of dsb sites using r package gkmsvm (https://cran.r-project.org/web/packages/gkmsvm). to learn the model, we mapped epigenomic data, dna motifs, and dna shape as follows. for epigenomic data including chip-seq and dnase-seq […]

PMCID: 5838836
PMID: 29618048
DOI: 10.1093/gigascience/gix136

[…] of the rnaseq mapped reads than the 3rd quartile, median, or mean mapped reads to reference enhancers in the villar dataset (log2 scale)., dregions with length shorter than 3000 bp (or 5000 bp) and gkmsvm scores ≥ median or mean scores for the villar enhancer dataset., eregions with more annotation terms (diversity of regulatory features) mapped to the regions, compared with those mapped […]

PMCID: 5773911
PMID: 29219068
DOI: 10.1186/s12859-017-1878-3

[…] are enriched in enhancers and have potential biological meaning. ghandi et al. improved kmer-svm by adopting another type of sequence features called gapped k-mers []. their method, known as gkmsvm, showed robustness in the estimation of k-mer frequencies and allowed higher performance than kmer-svm. however, k-mer features, though unbiased, may lack the ability to capture high order […]

PMCID: 5870572
PMID: 28881969
DOI: 10.1093/bioinformatics/btx234

[…] give an introduction to the datasets prepared for classification tasks and some details about model training procedure. then in section 3.2, we evaluate our method and compare its performance with gkmsvm and deepsea. next in section 3.3, we analyze k-mer embedding by probing into the k-mer statistics and visualizing the embedding vectors. additionally in section 3.4, we prove the effectiveness […]


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gkmSVM institution(s)
The Broad Institute of MIT and Harvard, Cambridge, MA, USA; School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran; Department of Engineering Science, College of Engineering, University of Tehran, and Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
gkmSVM funding source(s)
Supported by NIH grant R01 HG0007348 and grants from IPM (No. CS1391-4-02 and No. 94050016).

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