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For that purpose we modify the LogitBoost to obtain a version of the so-called blockwise boosting procedure for classification. It is shown that blockwise boosting has high potential in predictive proteomics.<\/jats:p>\n               <jats:p>Availability: R-code is freely available at http:\/\/www.statistik.lmu.de\/~gertheiss\/research.html.<\/jats:p>\n               <jats:p>Contact: \u00a0jan.gertheiss@stat.uni-muenchen.de<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp094","type":"journal-article","created":{"date-parts":[[2009,2,22]],"date-time":"2009-02-22T03:48:44Z","timestamp":1235274524000},"page":"1076-1077","source":"Crossref","is-referenced-by-count":8,"title":["Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting"],"prefix":"10.1093","volume":"25","author":[{"given":"Jan","family":"Gertheiss","sequence":"first","affiliation":[{"name":"Department of Statistics, Ludwig-Maximilians-Universit\u00e4t, Munich D-80799, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerhard","family":"Tutz","sequence":"additional","affiliation":[{"name":"Department of Statistics, Ludwig-Maximilians-Universit\u00e4t, Munich D-80799, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2009,2,20]]},"reference":[{"key":"2023051607020542800_B1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/bib\/bbn008","article-title":"newblock Machine learning methods for predictive proteomics","volume":"9","author":"Barla","year":"2008","journal-title":"Brief. 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