摘要
基因表达数据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,有助于对未知功能基因进行研究.基于判别的基因表达数据聚类方法具有无法准确确定类别的局限性,研究工作已转向具有更好聚类效果的基于模型的聚类方法.文中介绍了常见的基于模型的聚类方法及其特点,并就如何开发新的适合基因表达数据分析的基于模型的聚类算法进行了讨论.
Clustering analysis for gene expression data which is helpful to do research on genes with unkown function is the art of group genes with related functions according to the similarities in their expression profiles. In the past, similarity-based methods have been the primary clustering tool used to perform this task,but, a major limitation of these methods is their inability to determine the number of clusters. Therefore, attention is now turning to model-based approaches which has already showed to be more powerful in this task. In this paper, several popular model-based clustering algorithms and their characteristics are introduced and how to develop new model-based methods more suitable for gene expression data analysis are also discussed here.
出处
《江南大学学报（自然科学版）》
CAS
2006年第3期374-378,共5页
Joural of Jiangnan University (Natural Science Edition)
