几种微阵列基因表达数据分析方法的比较

Comparison of approaches in microarray gene expression data analysis

比较了微阵列基因表达数据处理中的几种方法,包括等级聚类、K-means方法、模糊聚类和自组织树.同时从算法中计算机的时空复杂度和结果的生物学意义两方面,对以上几种方法作了细致的讨论.结果显示,模糊聚类和自组织树都是较理想的方法.

This paper compares some approaches in microarray gene expression data analysis,including hierarchical clustering, K-means, fuzzy clustering and self-organized tree from space and time complexity of algorithms and biological significance of their results. Both fuzzy clustering and self-organized tree methods are ideal relatively.