The paper presents a comparison of the data-based and gene ontology (GO)-based approaches to cluster validation methods for gene microarray analysis. We apply a homogeneous approach to obtaining metrics from different GO-based similarity measures and a normalization of validation index values, that allows us to compare them to each other as well as to data-based validation indices. The results show strong correlation between both GO-based and data-based validation indices. The results suggest that this may represent an effective tool to support biomedical knowledge discovery tasks based on gene expression data

In a gene expression data matrix, a bicluster is a submatrix of genes and conditions that exhibits a high correlation of expression activity across both rows and columns. The problem of locating the most significant bicluster has been shown to be NP-complete. Heuristic approaches such as Cheng and Church's greedy node deletion algorithm have been previously employed. It is to be expected that stochastic search techniques such as evolutionary algorithms or simulated annealing might improve upon such greedy techniques. In this paper we show that an approach based on simulated annealing is well suited to this problem, and we present a comparative evaluation of simulated annealing and node deletion on a variety of datasets. We show that simulated annealing discovers more significant biclusters in many cases. Furthermore, we also test the ability of our technique to locate biologically verifiable biclusters within an annotated set of genes.

In a gene expression data matrix a bicluster is a grouping of a subset of genes and a subset of conditions which show correlating levels of expression activity. The difficulty of finding significant biclusters in gene expression data grows exponentially with the size of the dataset and heuristic approaches such as Cheng and Church's greedy node deletion algorithm are required. It is to be expected that stochastic search techniques such as genetic algorithms or simulated annealing might produce better solutions than greedy search. In this paper we show that a simulated annealing approach is well suited to this problem and we present a comparative evaluation of simulated annealing and node deletion on a variety of datasets. We show that simulated annealing discovers more significant biclusters in many cases.

UNLABELLED: In this paper we present a data mining system, which allows the application of different clustering and cluster validity algorithms for DNA microarray data. This tool may improve the quality of the data analysis results, and may support the prediction of the number of relevant clusters in the microarray datasets. This systematic evaluation approach may significantly aid genome expression analyses for knowledge discovery applications. The developed software system may be effectively used for clustering and validating not only DNA microarray expression analysis applications but also other biomedical and physical data with no limitations. AVAILABILITY: The program is freely available for non-profit use on request at http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html CONTACT: [email protected].

Summary: This paper presents an approach to assessing cluster validity based on similarity knowledge extracted from the Gene Ontology. Availability: The program is freely available for non-profit use on request from the authors. Contact: [email protected] Supplementary information: http://www.cs.tcd.ie/Nadia.Bolshakova/GOtool.html

Ensemble techniques have been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. Recently, analogous techniques for cluster analysis have been suggested. Research has demonstrated that, by combining a collection of dissimilar clusterings, an improved solution can be obtained. In this paper, we examine the potential of applying ensemble clustering techniques with a focus on the area of medical diagnostics. We present several ensemble generation and integration strategies, and evaluate each approach on a number of synthetic and real-world datasets. In addition, we show that diversity among ensemble members is necessary, but not sufficient to yield an improved solution without the selection of an appropriate integration method.

UNLABELLED: This paper presents a cluster validation tool for gene expression data. Machaon CVE (Clustering and Validation Environment) system aims to partition samples or genes into groups characterized by similar expression patterns, and to evaluate the quality of the clusters obtained. AVAILABILITY: The program is freely available for non-profit use on request at http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html SUPPLEMENTARY INFORMATION: http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html