{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:38:17Z","timestamp":1760549897735},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: 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.<\/jats:p>\n               <jats:p>Availability and implementation: LedPred is available on GitHub: https:\/\/github.com\/aitgon\/LedPred and Bioconductor: http:\/\/bioconductor.org\/packages\/release\/bioc\/html\/LedPred.html under the MIT license.<\/jats:p>\n               <jats:p>Contact: \u00a0aitor.gonzalez@univ-amu.fr<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv705","type":"journal-article","created":{"date-parts":[[2015,12,2]],"date-time":"2015-12-02T01:38:53Z","timestamp":1449020333000},"page":"1091-1093","source":"Crossref","is-referenced-by-count":3,"title":["LedPred: an R\/bioconductor package to predict regulatory sequences using support vector machines"],"prefix":"10.1093","volume":"32","author":[{"given":"Denis","family":"Seyres","sequence":"first","affiliation":[{"name":"1 INSERM, UMR1090 TAGC, Marseille, F-13288 France,"},{"name":"2 Aix-Marseille Universit\u00e9, UMR1090 TAGC, Marseille, F-13288 France,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elodie","family":"Darbo","sequence":"additional","affiliation":[{"name":"3 Cancer Research UK, London Research Institute, London WC2A 3LY, UK,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Perrin","sequence":"additional","affiliation":[{"name":"1 INSERM, UMR1090 TAGC, Marseille, F-13288 France,"},{"name":"2 Aix-Marseille Universit\u00e9, UMR1090 TAGC, Marseille, F-13288 France,"},{"name":"4 CNRS, Marseille, France and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carl","family":"Herrmann","sequence":"additional","affiliation":[{"name":"5 IPMB, Universit\u00e4t Heidelberg and Department of Theoretical Bioinformatics, DKFZ, Heidelberg 69120, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aitor","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"1 INSERM, UMR1090 TAGC, Marseille, F-13288 France,"},{"name":"2 Aix-Marseille Universit\u00e9, UMR1090 TAGC, Marseille, F-13288 France,"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,12,1]]},"reference":[{"key":"2023020112010403300_btv705-B1","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1038\/ng.1064","article-title":"Tissue-specific analysis of chromatin state identifies temporal signatures of enhancer activity during embryonic development","volume":"44","author":"Bonn","year":"2012","journal-title":"Nat. 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