UNLABELLED The expected correlation between genetic co-regulation and affiliation to a common biological process is not necessarily the case when numerical cluster algorithms are applied to gene expression data. GO-Cluster uses the tree structure of the Gene Ontology database as a framework for numerical clustering, and thus allowing a simple visualization of gene expression data at various lev...

To identify modules of interacting molecules often gene expression is analyzed with clustering methods. Constrained or semi-supervised clustering provides a framework to augment the primary, gene expression data with secondary data, to arrive at biological meaningful clusters. Here, we present an approach using constrained clustering and present favorable results on a biological data set of gen...

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the chall...

In this work, we assess the suitability of cluster analysis for the gene grouping problem confronted with microarray data. Gene clustering is the exercise of grouping genes based on attributes, which are generally the expression levels over a number of conditions or subpopulations. The hope is that similarity with respect to expression is often indicative of similarity with respect to much more...

In this paper, a similarity measure between genes with protein-protein interactions is proposed. The chip-chip data are converted into the same form of gene expression data with pearson correlation as its similarity measure. On the basis of the similarity measures of proteinprotein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure ...

MOTIVATION Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying pro...