We propose a measure for the validation of clusterings of gene expression data. This measure also useful to estimate missing gene expression levels, based the similarity information contained in a given clustering. It is shown that this measure is an improvement over the figure of merit, an existing validation measure especially developed for clusterings of gene expression data
Motivation: Microarray experiments generate a considerable amount of data, which analyzed properly h...
In the rapidly evolving field of genomics, many clustering and classification methods have been deve...
Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorit...
We propose a measure for the validation of clusterings of gene expression data. This measure also us...
Abstract. Motivation: Many clustering algorithms have been proposed for the analysis of gene expr...
Abstract Background Cluster analysis is an integral part of high dimensional data analysis. In the c...
Thesis (Ph. D.)--University of Washington, 2001The invention of DNA microarrays allows us to study s...
Motivation: A measurement of cluster quality is needed to choose potential clusters of genes that co...
AbstractClustering algorithms have been shown to be useful to explore large-scale gene expression pr...
Motivation: Unsupervised learning or clustering is frequently used to explore gene expression profil...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency i...
Gene expression data hide vital information required to understand the biological process that takes...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in ...
Abstract: We assess the robustness of partitional clustering algorithms applied to gene expression d...
In this work a multi-step approach for clustering assessment, visualization and data validation is ...
Motivation: Microarray experiments generate a considerable amount of data, which analyzed properly h...
In the rapidly evolving field of genomics, many clustering and classification methods have been deve...
Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorit...
We propose a measure for the validation of clusterings of gene expression data. This measure also us...
Abstract. Motivation: Many clustering algorithms have been proposed for the analysis of gene expr...
Abstract Background Cluster analysis is an integral part of high dimensional data analysis. In the c...
Thesis (Ph. D.)--University of Washington, 2001The invention of DNA microarrays allows us to study s...
Motivation: A measurement of cluster quality is needed to choose potential clusters of genes that co...
AbstractClustering algorithms have been shown to be useful to explore large-scale gene expression pr...
Motivation: Unsupervised learning or clustering is frequently used to explore gene expression profil...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency i...
Gene expression data hide vital information required to understand the biological process that takes...
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in ...
Abstract: We assess the robustness of partitional clustering algorithms applied to gene expression d...
In this work a multi-step approach for clustering assessment, visualization and data validation is ...
Motivation: Microarray experiments generate a considerable amount of data, which analyzed properly h...
In the rapidly evolving field of genomics, many clustering and classification methods have been deve...
Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorit...