Cluster analysis of gene expression data based on self-splitting and merging competitive learning.

Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new cl...

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發表在:	IEEE Transactions on information technology in biomedicine 8, 1 (2004).
主要作者:	Shuanhu Wu
格式:	Article
語言:	English
主題:	Biologically relevant groups. cDNA microarray. Cluster self-splitting learning. Clustering algorithms. Clustering framework. Gene expression data cluster analysis. Gene expression data simulation. Gene expression fundamental patterns. Genes identification. Merging competitive learning paradigm. Natural clusters. One-prototype-take-one-cluster. Yeast cell cycle.