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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Bibliographic Details

Published in:	IEEE Transactions on information technology in biomedicine 8, 1 (2004).
Main Author:	Shuanhu Wu
Format:	Article
Language:	English
Subjects:	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.