Aggregation Method for Gene Mining Based on Mutual Information Network
Abstract
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.
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