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.
|Author||Elodie Darbo, Denis Seyres, Aitor Gonzalez|
|Bioconductor views||ChIPSeq Classification MotifAnnotation Sequencing Software SupportVectorMachine|
|Maintainer||Aitor Gonzalez <firstname.lastname@example.org>|
|License||MIT | file LICENSE|
|Package repository||View on Bioconductor|
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