基因表达聚类分析技术的现状与发展

随着多个生物基因组测序的完成、DNA芯片技术的广泛应用，基因表达数据分析已成为后基因组时代的研究热点.聚类分析能将功能相关的基因按表达谱的相似程度归纳成类，有助于对未知功能的基因进行研究，是目前基因表达分析研究的主要计算技术之一.已有多种聚类分析算法用于基因表达数据分析，各种算法因其着眼点、原理等方面的差异，而各有其优缺点.如何对各种聚类算法的有效性进行分析、并开发新型的、适合于基因表达数据分析的方法已是当务之急.

Actuality and Development of The Clustering Technologies for Gene Expression

With many genomes completed and extensive applications of DNA chips, analysis of the gene expression data has become a hotspot in the postgenomic age. Clustering is the art to group genes with related functions according to the similarities in their expression profiles. A number of clustering algorithms have been developed for gene expression data analysis. For their respective focuses and principles, every method has its own advantages and disadvantages, which are reviewed. How to evaluate the capabilities of these algorithms, and to develop new methods more suitable for gene expression analysis, should be urgent.