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Microarray studies are used in molecular biology to explore patterns of expression of thousands of genes. This methodology has relevantly developed in the last decades, and so has the need for appropriate methods for analyzing high-throughput data generated from such experiments. Identifying sets of genes and samples characterized by similar values of expression and validating these results are two...
Cluster analysis is an important tool for discovering the structures and patterns hidden in gene expression data. In this paper, a new algorithm for clustering gene expression profiles is proposed. In this method, we find natural clusters in the data based on a competitive learning strategy. Using partially known modes as constraints in our method, we reduce the sensitivity of the clustering procedure...
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