title: LS-GKM: a new gkm-SVM for large-scale datasets.
abstract: gkm-SVM is a sequence-based method for predicting and detecting the regulatory vocabulary encoded in functional DNA elements, and is a commonly used tool for studying gene regulatory mechanisms. Here we introduce new software, LS-GKM, which removes several limitations of our previous releases, enabling training on much larger scale (LS) datasets. LS-GKM also provides additional advanced gapped k-mer based kernel functions. With these improvements, LS-GKM achieves considerably higher accuracy than the original gkm-SVM.
  
  
  
  C/C ++ source codes and related scripts are freely available from http://github.com/Dongwon-Lee/lsgkm/, and supported on Linux and Mac OS X.
  
  
  
  dwlee@jhu.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
authors.forename: Dongwon
authors.initials: D
authors.lastname: Lee
mesh_terms: D047369:Chromatin Immunoprecipitation; D004247:DNA / Q000235:genetics; D053263:Gene Regulatory Networks; D006801:Humans; D012984:Software; D060388:Support Vector Machine
chemical_list: D004247:DNA