SVM在基因表达数据分类中的研究和应用

介绍了一种使用基因芯片实验产生的基因表达数据对功能基因进行分类的方法,该方法是以支持向量机(SVM)理论为基础的。文中描述了径向基函数SVM,与其它SVM相比,径向基函数SVM在基因分类中有更好的性能。SVM的理论基础是统计学习理论,它不仅结构简单,而且技术性能高,泛化能力强,在基因表达式分类中表现出有很多优点,成为热点研究方向。

Research and Application of SVM in Classification of Gene Expression Data

Introduce a method of functionally classifying genes using gene expression data from DNA microarray hybridization experiments.The method is based on the theory of support vector machine(SVMs).Describe SVMs that uses different similarity metrics including a simple dot product of gene expression vectors,polynomial version of the dot product,and a radial basis function.Compared to the other SVM similarity metrics,the radial basis function SVM appears to provide superior performance in identifying sets of genes with a common function using expression data.In addition,SVM performance is compared to four standard machine learning algorithms.SVMs have many features that make them attractive for gene expression analysis,including their flexibility in chosing a similarity function,sparseness of solution when dealing with large data sets,the ability to handle large feature spaces,and the ability to identify outliers.