Afsari Bahman, Fertig Elana J, Geman Donald, Marchionni Luigi
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.
Bioinformatics. 2015 Jan 15;31(2):273-4. doi: 10.1093/bioinformatics/btu622. Epub 2014 Sep 26.
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.
The 'switchBox' package is freely available from Bioconductor: http://www.bioconductor.org.
Supplementary data are available at Bioinformatics online.
k-高分对（kTSP）是一种基于一组配对测量值从高通量数据进行预测的分类方法。一对测量值的两种可能排序（例如两个基因表达的反转）中的每一种都与两个类别之一相关联。kTSP预测规则是基于顺序切换的此类单个双特征决策规则之间投票的汇总。kTSP与其前身高分对（TSP）一样，是一种无参数分类器，仅依赖于一小部分特征的排序，使其对噪声具有鲁棒性，并且可能易于从生物学角度进行解释。与TSP不同，kTSP与标准基因组学分类技术（包括支持向量机和微阵列预测分析）具有相当的准确性。在这里，我们描述了“switchBox”，一个用于基于kTSP进行预测的R包。
“switchBox”包可从Bioconductor免费获得：http://www.bioconductor.org。
补充数据可在《生物信息学》在线获取。
