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Aggregation Method for Gene Mining Based on Mutual Information Network

Hongjuan Zhao, Qingyun Wang, Bo Li, Qizhi Fang


Increasing number of methodologies are developed to understand functional genomic correlations from mRNA express data. In this work, we propose a method that combines rank aggregation with mutual information relevance network to identify differentially coexpressed key genes. For two expression data profiles from experimental and control sample groups, we construct mutual information networks G1 and G2, respectively, and define
several structural parameter of the network. All the parameters are heterogeneous and yield to different ranks of genes, in which top-ranked genes are more important w.r.t. the corresponding parameter. In order to select the functional key genes with overall significance, rank aggregation technique are employed to integrate the different ranks to a final “super-list”. Finally the expression profiles of yeast Saccharomyces cerevisiae downloaded
from NCBI are used to test the effectiveness of our method.


mutual information, relevance network, structural parameters, rank aggregation.

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