Constrained clusters of gene expression profiles with pathological features

Bioinformatics
Jun SeseShinichi Morishita

Abstract

Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, since the first clustering process and the second classification step, in which the features are associated with clusters, are performed independently, the initial set of clusters may omit genes that are associated with pathologically meaningful features. Therefore, it is important to devise a way of identifying gene expression clusters that are associated with pathological features. We present the novel technique of 'itemset constrained clustering' (IC-Clustering), which computes the optimal cluster that maximizes the interclass variance of gene expression between groups, which are divided according to the restriction that only divisions that can be expressed using common features are allowed. This constraint automatically labels each cluster with a set of pathological features which characterize that cluster. When applied to liver cancer datasets, IC...Continue Reading

Citations

Nov 15, 2007·DNA Research : an International Journal for Rapid Publication of Reports on Genes and Genomes·Sachiko IsobeSatoshi Tabata
Dec 22, 2006·BMC Genomics·Kurt FellenbergJörg D Hoheisel
Dec 21, 2005·Proceedings of the National Academy of Sciences of the United States of America·Yoshikazu OhyaShinichi Morishita
May 25, 2013·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Itziar IrigoienConcepción Arenas
May 14, 2014·Statistical Methods in Medical Research·Itziar Irigoien, Concepción Arenas

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