Package: LedPred
Title: Learning from DNA to Predict Enhancers
Description: 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.
Version: 1.47.0
Date: 2016-08-13
Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez
Maintainer: Aitor Gonzalez <aitor.gonzalez@univ-amu.fr>
Depends: R (>= 3.2.0), e1071 (>= 1.6)
Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl,
        ROCR, testthat
License: MIT | file LICENSE
LazyData: true
Packaged: 2026-07-04 02:19:03 UTC; root
biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq,
        Sequencing, Classification
NeedsCompilation: no
BugReports: https://github.com/aitgon/LedPred/issues
RoxygenNote: 5.0.1
Config/pak/sysreqs: cmake make libuv1-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:41:34 UTC
RemoteUrl: https://github.com/bioc/LedPred
RemoteRef: HEAD
RemoteSha: 1cfe7242af3b7acc2d07a4ec6f37f9ebddc30633