Reference: Cheng L, et al. (2013) Gene interaction networks based on kernel correlation metrics. Int J Comput Biol Drug Des 6(1-2):72-92

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Abstract


In this paper, a Kernel correlation coefficient (KCC) method is proposed to elucidate the gene nonlinear relationships as a distance metric. To evaluate the performance of this nonlinear distance measure, a biological network of the Gaussian Kernel on a public dataset of yeast genes is constructed by using a graph theory. Specifically, the distribution and properties of this new measure are analysed and compared with the classical Pearson correlation method. The reliability and advantages of our proposed Kernel correlation metric is verified and shown formally on ten showcases of the DREAM (Dialogue for Reverse Engineering Assessments and Methods) project. Test experiment results demonstrate that the proposed Kernel correlation coefficient measure has a strong capability in identifying interaction genes, and that the proposed method can detect accurately the key genes and functional interactions (also known as the cliques) as compared to the commonly used Pearson correlation and Mutual Information measures.

Reference Type
Journal Article | Research Support, Non-U.S. Gov't
Authors
Cheng L, Khorasani K, Ding Y, Guo X
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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

Phenotype Annotations


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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

Disease Annotations


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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

Interaction Annotations


Genetic Interactions

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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