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LedPred: an R/bioconductor package to predict regulatory sequences using support vector machines

Abstract : Supervised classification based on support vector machines (SVMs) has successfully been used for the prediction of cis-regulatory modules (CRMs). However, no integrated tool using such heterogeneous data as position-specific scoring matrices, ChIP-seq data or conservation scores is currently available. Here, we present LedPred, a flexible SVM workflow that predicts new regulatory sequences based on the annotation of known CRMs, which are associated to a large variety of feature types. LedPred is provided as an R/Bioconductor package connected to an online server to avoid installation of non-R software. Due to the heterogeneous CRM feature integration, LedPred excels at the prediction of regulatory sequences in Drosophila and mouse datasets compared with similar SVM-based software.
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https://hal-amu.archives-ouvertes.fr/hal-01460122
Contributor : Lionel Spinelli <>
Submitted on : Tuesday, February 7, 2017 - 4:00:07 PM
Last modification on : Wednesday, July 10, 2019 - 7:14:02 PM

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Denis Seyres, Elodie Darbo, Laurent Perrin, Carl Herrmann, Aitor Gonzalez. LedPred: an R/bioconductor package to predict regulatory sequences using support vector machines. Bioinformatics, Oxford University Press (OUP), 2016, 32 (7), pp.1091-1093. ⟨10.1093/bioinformatics/btv705⟩. ⟨hal-01460122⟩

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