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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "LedPred" in publications use:'
type: software
license: MIT
title: 'LedPred: Learning from DNA to Predict Enhancers'
version: 1.39.0
doi: 10.1093/bioinformatics/btv705
abstract: This package aims at creating a predictive model of regulatory sequences
  used to score unknown sequences based on the content of DNA motifs, next-generation
  sequencing (NGS) peaks and signals and other numerical scores of the sequences using
  supervised classification. The package contains a workflow based on the support
  vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters
  and feature number and creates a model that can be stored and used to score the
  regulatory potential of unknown sequences.
authors:
- family-names: Gonzalez
  given-names: Aitor
  email: aitor.gonzalez@univ-amu.fr
- family-names: Darbo
  given-names: Elodie
- family-names: Seyres
  given-names: Denis
preferred-citation:
  type: article
  title: 'LedPred: An R/Bioconductor package to predict regulatory sequences using
    support vector machines'
  authors:
  - family-names: Seyre
    given-names: Denis
  - family-names: Darbo
    given-names: Elodie
  - family-names: Perrin
    given-names: Laurent
  - family-names: Herrmann
    given-names: Carl
  - family-names: Gonzalez
    given-names: Aitor
    email: aitor.gonzalez@univ-amu.fr
  year: '2015'
  journal: Bioinformatics
  doi: 10.1093/bioinformatics/btv705
  url: http://bioinformatics.oxfordjournals.org/content/early/2016/01/14/bioinformatics.btv705.long
repository: https://bioc.r-universe.dev
repository-code: https://github.com/aitgon/LedPred
url: https://github.com/aitgon/LedPred
date-released: '2016-08-13'
contact:
- family-names: Gonzalez
  given-names: Aitor
  email: aitor.gonzalez@univ-amu.fr