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  "Title": "Learning from DNA to Predict Enhancers",
  "Description": "This package aims at creating a predictive model of\nregulatory sequences used to score unknown sequences based on\nthe content of DNA motifs, next-generation sequencing (NGS)\npeaks and signals and other numerical scores of the sequences\nusing supervised classification. The package contains a\nworkflow based on the support vector machine (SVM) algorithm\nthat maps features to sequences, optimize SVM parameters and\nfeature number and creates a model that can be stored and used\nto score the regulatory potential of unknown sequences.",
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  "Date": "2016-08-13",
  "Author": "Elodie Darbo, Denis Seyres, Aitor Gonzalez",
  "Maintainer": "Aitor Gonzalez <aitor.gonzalez@univ-amu.fr>",
  "License": "MIT | file LICENSE",
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