title: switchBox: an R package for k-Top Scoring Pairs classifier development.
abstract: k-Top 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 'switchBox', an R package for kTSP-based prediction.
  
  
  BACKGROUND
  The 'switchBox' package is freely available from Bioconductor: http://www.bioconductor.org.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Bahman
authors.initials: B
authors.lastname: Afsari
authors.affiliation: Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Elana J
authors.initials: EJ
authors.lastname: Fertig
authors.affiliation: Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Donald
authors.initials: D
authors.lastname: Geman
authors.affiliation: Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 and Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Luigi
authors.initials: L
authors.lastname: Marchionni
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000145:classification* / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000235:genetics; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor