Reference: Famili AF, et al. (2004) Evaluation and optimization of clustering in gene expression data analysis. Bioinformatics 20(10):1535-45

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Abstract


Motivation: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis.

Results: We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be performed efficiently. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated datasets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters.

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Reference Type
Comparative Study | Evaluation Study | Journal Article | Validation Study
Authors
Famili AF, Liu G, Liu Z
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Gene Ontology Annotations


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Gene/Complex Qualifier Gene Ontology Term Aspect Annotation Extension Evidence Method Source Assigned On Reference

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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

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Gene Disease Ontology Term Qualifier Evidence Method Source Assigned On Reference

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Post-translational Modifications


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Site Modification Modifier Reference

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Interactor Interactor Allele Assay Annotation Action Phenotype SGA score P-value Source Reference

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Interactor Interactor Assay Annotation Action Modification Source Reference

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Gene Species Gene ID Strain background Direction Details Source Reference