For better understanding of genetic mechanisms underlying clinical observations, we often want to determine which genes and clinical traits are interrelated. We introduce a computational method that can find co-clusters or groups of genes and clinical parameters that are believed to be closely related to each other based upon given empirical information. The proposed method was tested with data from an acute myelogenous leukemia (AML) study and identified statistically significant co-clusters of genes and clinical traits. The validation of our results with gene ontology (GO) as well as the literature suggest that the proposed method can provide biologically meaningful co-clusters of genes and traits

Finding Co-Clusters of Genes and Clinical Parameters

BENINI, LUCA;
2006

Abstract

For better understanding of genetic mechanisms underlying clinical observations, we often want to determine which genes and clinical traits are interrelated. We introduce a computational method that can find co-clusters or groups of genes and clinical parameters that are believed to be closely related to each other based upon given empirical information. The proposed method was tested with data from an acute myelogenous leukemia (AML) study and identified statistically significant co-clusters of genes and clinical traits. The validation of our results with gene ontology (GO) as well as the literature suggest that the proposed method can provide biologically meaningful co-clusters of genes and traits
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
906
912
S. Yoon; L. Benini; G. De Micheli
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/97104
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