Li Yi, Campbell Colin, Tipping Michael
Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK Microsoft Research, 7 J J Thomson Avenue, Cambridge, CB3 0FD, UK.
Bioinformatics. 2002 Oct;18(10):1332-9. doi: 10.1093/bioinformatics/18.10.1332.
We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays.
We demonstrate the effectiveness of the algorithms on three gene expression datasets for cancer, showing they compare well with alternative kernel-based techniques. By automatically incorporating feature selection, accurate classifiers can be constructed utilizing very few features and with minimal hand-tuning. We argue that the feature selection is meaningful and some of the highlighted genes appear to be medically important.
我们研究了两种结合特征选择的新型贝叶斯分类算法。这些算法被应用于对来自cDNA微阵列的基因表达数据进行分类。
我们在三个癌症基因表达数据集上证明了这些算法的有效性，表明它们与基于核的替代技术相比具有优势。通过自动纳入特征选择，可以使用极少的特征并经过最少的人工调整来构建准确的分类器。我们认为特征选择是有意义的，并且一些被突出显示的基因似乎具有医学重要性。
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