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Although many methods have been proposed in the literature, their performance in terms of recall (sensitivity) and precision (predictive positive value) is limited in a context where the number of variables by far exceeds the number of observations or in a highly correlated setting.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we propose a general algorithm, which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data. We then apply it in two different ways to improve biological network reverse-engineering.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Code is available as the SelectBoost package on the CRAN, https:\/\/cran.r-project.org\/package=SelectBoost. Some network reverse-engineering functionalities are available in the Patterns CRAN package, https:\/\/cran.r-project.org\/package=Patterns.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa855","type":"journal-article","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T11:19:17Z","timestamp":1600687157000},"page":"659-668","source":"Crossref","is-referenced-by-count":5,"title":["selectBoost: a general algorithm to enhance the performance of variable selection methods"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0837-8281","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric","family":"Bertrand","sequence":"first","affiliation":[{"name":"Institut de Recherche Math\u00e9matique Avanc\u00e9e, CNRS UMR 7501, Labex IRMIA, Universit\u00e9 de Strasbourg , Strasbourg, France"},{"name":"Universit\u00e9 de Technologie de Troyes, ICD, ROSAS, M2S , Troyes, France"}]},{"given":"Isma\u00efl","family":"Aouadi","sequence":"additional","affiliation":[{"name":"ImmunoRhumatologie Mol\u00e9culaire, INSERM UMR_S 1109, LabEx TRANSPLANTEX, Centre de Recherche d\u2019Immunologie et d\u2019H\u00e9matologie, F\u00e9d\u00e9ration de M\u00e9decine Translationnelle de Strasbourg (FMTS), Universit\u00e9 de Strasbourg , Strasbourg, France"},{"name":"Laboratoire International Associ\u00e9 (LIA) INSERM, Strasbourg (France) - 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Nagano (Japan) , Strasbourg, France"},{"name":"F\u00e9d\u00e9ration Hospitalo-Universitaire (FHU) OMICARE, Laboratoire Central d\u2019Immunologie, P\u00f4le de Biologie, Nouvel H\u00f4pital Civil, H\u00f4pitaux Universitaires de Strasbourg , Strasbourg, France"}]},{"given":"Laurent","family":"Vallat","sequence":"additional","affiliation":[{"name":"ImmunoRhumatologie Mol\u00e9culaire, INSERM UMR_S 1109, LabEx TRANSPLANTEX, Centre de Recherche d\u2019Immunologie et d\u2019H\u00e9matologie, F\u00e9d\u00e9ration de M\u00e9decine Translationnelle de Strasbourg (FMTS), Universit\u00e9 de Strasbourg , Strasbourg, France"},{"name":"F\u00e9d\u00e9ration Hospitalo-Universitaire (FHU) OMICARE, Laboratoire Central d\u2019Immunologie, P\u00f4le de Biologie, Nouvel H\u00f4pital Civil, H\u00f4pitaux Universitaires de Strasbourg , Strasbourg, France"}]},{"given":"Seiamak","family":"Bahram","sequence":"additional","affiliation":[{"name":"ImmunoRhumatologie Mol\u00e9culaire, INSERM UMR_S 1109, LabEx TRANSPLANTEX, Centre de Recherche d\u2019Immunologie et d\u2019H\u00e9matologie, F\u00e9d\u00e9ration de M\u00e9decine Translationnelle de Strasbourg (FMTS), Universit\u00e9 de Strasbourg , Strasbourg, France"},{"name":"Laboratoire International Associ\u00e9 (LIA) INSERM, Strasbourg (France) - 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