We propose a method for global validation of gene clusterings. The method selects a set of informative and non-redundant GO terms through an exploration of the Gene Ontology structure guided by mutual information. Our approach yields a global assessment of the clustering quality, and a higher level interpretation for the clusters, as it relates GO terms with specific clusters. We show that in two gene expression data sets our method offers an improvement over previous approaches
quality SUMMARY Motivation: Traditional gene clustering algorithms focus only on the raw expression ...
Abstract. With the invention of biotechnological high throughput methods like DNA microarrays, biolo...
Abstract Background A cluster analysis is the most commonly performed procedure (often regarded as a...
We propose a method for global validation of gene clusterings. The method selects a set of informati...
Abstract. We propose a method for global validation of gene cluster-ings. The method selects a set o...
Based on the correlation between expression and ontology-driven gene similarity, we incorporate func...
Based on the correlation between expression and ontology-driven gene similarity, we incorporate func...
Background: The biological interpretation of large-scale gene expression data is one of the paramoun...
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This...
Abstract:- In this paper it is explained a new approach for clustering Gene Ontology (GO) terms by e...
Motivation: With the advent of DNA microarray technologies, the parallel quantification of genome-wi...
Abstract. The huge volume of gene expression data produced by mi-croarrays and other high-throughput...
Understanding biological activity requires the detection of crucial proteins. The identification of ...
Abstract. Motivation: Many clustering algorithms have been proposed for the analysis of gene expr...
In this work a multi-step approach for clustering assessment, visualization and data validation is ...
quality SUMMARY Motivation: Traditional gene clustering algorithms focus only on the raw expression ...
Abstract. With the invention of biotechnological high throughput methods like DNA microarrays, biolo...
Abstract Background A cluster analysis is the most commonly performed procedure (often regarded as a...
We propose a method for global validation of gene clusterings. The method selects a set of informati...
Abstract. We propose a method for global validation of gene cluster-ings. The method selects a set o...
Based on the correlation between expression and ontology-driven gene similarity, we incorporate func...
Based on the correlation between expression and ontology-driven gene similarity, we incorporate func...
Background: The biological interpretation of large-scale gene expression data is one of the paramoun...
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This...
Abstract:- In this paper it is explained a new approach for clustering Gene Ontology (GO) terms by e...
Motivation: With the advent of DNA microarray technologies, the parallel quantification of genome-wi...
Abstract. The huge volume of gene expression data produced by mi-croarrays and other high-throughput...
Understanding biological activity requires the detection of crucial proteins. The identification of ...
Abstract. Motivation: Many clustering algorithms have been proposed for the analysis of gene expr...
In this work a multi-step approach for clustering assessment, visualization and data validation is ...
quality SUMMARY Motivation: Traditional gene clustering algorithms focus only on the raw expression ...
Abstract. With the invention of biotechnological high throughput methods like DNA microarrays, biolo...
Abstract Background A cluster analysis is the most commonly performed procedure (often regarded as a...