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.
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