Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting
Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting
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DOI：
10.1093/bioinformatics/btp094
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发表时间：
2009-04
期刊：
影响因子：
5.8
通讯作者：
J. Gertheiss;G. Tutz
中科院分区：
文献类型：
--
作者：
J. Gertheiss;G. Tutz
当质谱学中的特征选择基于单一的m/z值时，问题产生于这样的事实，即可变性不仅在垂直方向上，而且在水平方向上，即稍微不同的m/z值也可能对应于相同的特征。因此，我们建议使用全谱作为分类器的输入，但选择相邻m/z值的小组或小块，而不仅仅是单个m/z值。为此，我们修改LogitBoost以获得用于分类的所谓的块增强过程的版本。这表明，区块增强在预测蛋白质组学中具有很高的潜力。
When feature selection in mass spectrometry is based on single m/z values, problems arise from the fact that variability is not only in vertical but also in horizontal direction, i.e. also slightly differing m/z values may correspond to the same feature. Hence, we propose to use the full spectra as input to a classifier, but to select small groups -- or blocks -- of adjacent m/z values, instead of single m/z values only. 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.