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...

Szczegółowa specyfikacja

Opis bibliograficzny

Wydane w:	IEEE Transactions on information technology in biomedicine 8, 1 (2004).
1. autor:	Shuanhu Wu
Format:	Artykuł
Język:	English
Hasła przedmiotowe:	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.