Reference: Cheng Y and Church GM (2000) Biclustering of expression data. Proc Int Conf Intell Syst Mol Biol 8:93-103

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Abstract


An efficient node-deletion algorithm is introduced to find submatrices in expression data that have low mean squared residue scores and it is shown to perform well in finding co-regulation patterns in yeast and human. This introduces "biclustering", or simultaneous clustering of both genes and conditions, to knowledge discovery from expression data. This approach overcomes some problems associated with traditional clustering methods, by allowing automatic discovery of similarity based on a subset of attributes, simultaneous clustering of genes and conditions, and overlapped grouping that provides a better representation for genes with multiple functions or regulated by many factors.

Reference Type
Journal Article
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Cheng Y, Church GM
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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