Abstract
Most of the existing methods in literature have used proximity measures in the construction of co-expression networks (CEN) consisting of functional gene modules. This work describes the construction of co-expression network using mutual information (MI) as a proximity measure with non-linear correlation. The network modules are extracted that are defined over a subset of samples. This method has been tested on several publicly available datasets and the subspace network modules obtained have been validated in terms of both internal and external measures.
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