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dc.contributor.author曹晖
dc.contributor.author席斌
dc.contributor.author米红
dc.date.accessioned2017-11-14T03:19:30Z
dc.date.available2017-11-14T03:19:30Z
dc.date.issued2007-06-21
dc.identifier.citation计算机工程与应用,2007,(18):238-242
dc.identifier.issn1002-8331
dc.identifier.otherJSGG200718072
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/156706
dc.description.abstract自组织特征映射神经网络与层次聚类算法是两种较经典的分析基因表达数据的聚类算法,但由于基因表达数据的复杂性与不稳定性,这两种算法都存在着自身的优劣。因此,在比较两种算法差异性的基础上,创造性地提出了一种新算法,即通过SOM算法对基因表达数据进行聚类,再用层次聚类将每个类对应的神经元权值二次聚类,并将此算法应用在酵母菌基因表达数据中,用实验证明改进算法克服了自组织算法的一些缺陷,提高了基因聚类的效能。
dc.description.abstractSelf-Organizing Maps(SOM) and the hierarchical clustering are two of the most classical clustering technologies for analyzing gene expression data,which exist own advantages and disadvantages on account of the complexity and the instability of gene expression data.Therefore,on base of comparing difference of the two clustering technologies,this article creatively proposes one new algorithm,that is first clustering gene expression data with SOM and second clustering the weight of nerve cells corresponding the clustering from the first step.In succession,the new algorithm is applied to the published data of yeast gene expression to prove that it conquers some bug of SOM and improves the efficiency of gene clustering through emulation mode.
dc.description.sponsorship厦门大学985研究项目。
dc.language.isozh_CN
dc.subjectSOM算法
dc.subject层次聚类
dc.subject基因表达数据
dc.subjectSelf-Organizing Maps(SOM) algorithm
dc.subjecthierarchical clustering
dc.subjectgene expression data
dc.title一种新聚类算法在基因表达数据分析中的应用
dc.title.alternativeApplication of new clustering algorithms in gene expression data
dc.typeArticle


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