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聚类算法在基因表达数据分析中的应用 被引量：4

Application of Clustering Algorithms to the Analysis of Gene Expression Data
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摘要 聚类算法在基因表达数据的分析处理中得到日益广泛的应用 .文中对几种典型的聚类算法进行描述 ,对各算法在基因表达数据处理中的特点 ,进行评价并提出改进的策略 .最后 ,指出聚类算法在生物信息学应用中的发展趋势 . Clustering algorithms have become increasingly important in analyzing and processing gene expression data. Several typical clustering methods are described here. After estimating the characteristic of clustering methods in processing gene expression data, some strategies for its improvement are proposed; and the trend of applying clustering algorithms to bioinformatics is pointed out.
作者 朱婵 许龙飞
机构地区 暨南大学信息科技学院
出处 《华侨大学学报（自然科学版）》 CAS 北大核心 2005年第1期7-10,共4页 Journal of Huaqiao University(Natural Science)
基金 国家自然科学基金资助项目 (60 3 740 70 ) 广东省自然科学基金资助项目 (0 3 1 90 3 )
关键词 生物信息学 基因表达数据 聚类算法 bioinformatics, gene expression, data, clustering algorithm
分类号 Q786 [生物学—分子生物学]
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参考文献11

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二级参考文献19

  • 1Lukashin AV, Fuchs R. Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters[J]. Bioinformatics, 2001, 17: 405-414.
  • 2Dudoit S, Yang YH, Callow MJ, et al. Statistical methods for identifying differenctially expressed genes in replicated cDNA microarray experiments[J]. Statistica Sinica, 2002,12:111-139.
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  • 10Bozinov D, Rahnenfuhrer J. Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering[J]. Bioinformatics, 2002,18: 747-756.

共引文献25

同被引文献31

引证文献4

二级引证文献10

华侨大学学报（自然科学版）
2005年 第1期

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