支持向量机和神经网络作为肿瘤基因分析的标记选择器

2006 3rd International IEEE Conference Intelligent Systems Pub Date : 2006-09-01 DOI:10.1109/IS.2006.348492

M. Blazadonakis, M. Zervakis, M. Kounelakis, E. Biganzoli, Nicola Lama

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引用次数: 7

摘要

DNA微阵列分析使我们能够在单个实验中同时研究数千个基因的表达水平。标记选择问题已经得到了广泛的研究，但在本文中我们也考虑了所选标记的质量。因此，我们解决了选择一个小的基因子集的问题，这些基因子集足以在分类中区分两个感兴趣的类别，同时保留自相似特征以允许每个类别内的封闭聚类

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Support Vector Machines and Neural Networks as Marker Selectors for Cancer Gene Analysis

DNA micro-array analysis allows us to study the expression level of thousands of genes simultaneously on a single experiment. The problem of marker selection has been extensively studied but in this paper we also consider the quality of the selected markers. Thus, we address the problem of selecting a small subset of genes that would be adequate enough to discriminate between the two classes of interest in classification, while preserving self-similar characteristics to allow closed clustering within each class

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2006 3rd International IEEE Conference Intelligent Systems

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