Consensus clustering greatly improves the accuracy of identifying cluster group membership based solely on the gene-expression vector, but as with other clustering algorithms still produces essentially unannotated clusters which require further external validation by gene function analysis.
Nov 1, 2004
Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis.
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Consensus clustering and functional interpretation of gene expression data
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Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene expression analysis. Here we introduce Consensus ...
Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus ...
The biological function of an unannotated gene is predicted based on the most enriched functional category in its consensus cluster. The MIPS gene annotations ...
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based classification method has a significantly better predictive performance than a previously used clustering-based classification method while performing ...
Mar 23, 2024 · In protein function prediction, clustering can help in grouping proteins based on their structure, sequence, or functional properties. In ...
Aug 5, 2022 · In this study, we adopted the consensus clustering approach to classify patients with AIS into molecular subgroups based on the transcriptomic profiles of ...
Abstract. In this paper we present a new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data.
Mar 23, 2024 · Consensus clustering enhances the robustness of cluster analysis by focusing on consistent patterns and can include stability scores to ...