{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:45:28Z","timestamp":1783766728638,"version":"3.55.0"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary : k\u2013Top Scoring Pairs ( kTSP ) is a classification method for prediction from high-throughput data based on a set of the paired measurements. Each of the two possible orderings of a pair of measurements (e.g. a reversal in the expression of two genes) is associated with one of two classes. The kTSP prediction rule is the aggregation of voting among such individual two-feature decision rules based on order switching. kTSP , like its predecessor, Top Scoring Pair ( TSP ), is a parameter-free classifier relying only on ranking of a small subset of features, rendering it robust to noise and potentially easy to interpret in biological terms. In contrast to TSP , kTSP has comparable accuracy to standard genomics classification techniques, including Support Vector Machines and Prediction Analysis for Microarrays. Here, we describe \u2018switchBox\u2019, an R package for kTSP -based prediction.<\/jats:p>\n               <jats:p>Availability : The \u2018switchBox\u2019 package is freely available from Bioconductor: http:\/\/www.bioconductor.org .<\/jats:p>\n               <jats:p>Contact : bahman@jhu.edu<\/jats:p>\n               <jats:p>Supplementary information : Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu622","type":"journal-article","created":{"date-parts":[[2014,9,28]],"date-time":"2014-09-28T00:25:27Z","timestamp":1411863927000},"page":"273-274","source":"Crossref","is-referenced-by-count":50,"title":["switchBox: an R package for k\u2013Top Scoring Pairs classifier development"],"prefix":"10.1093","volume":"31","author":[{"given":"Bahman","family":"Afsari","sequence":"first","affiliation":[{"name":"1 Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and 2 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elana J.","family":"Fertig","sequence":"additional","affiliation":[{"name":"1 Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and 2 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donald","family":"Geman","sequence":"additional","affiliation":[{"name":"1 Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and 2 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"Marchionni","sequence":"additional","affiliation":[{"name":"1 Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and 2 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2014,9,26]]},"reference":[{"key":"2023020116155910900_btu622-B1","doi-asserted-by":"crossref","DOI":"10.1214\/14-AOAS738","article-title":"Rank discriminants for predicting phenotypes from RNA expression","author":"Afsari","year":"2014","journal-title":"Ann. 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