Single-shot determination of differential gene network on multiple disease subtypes

A differential gene expressional network determines the prominent genes under altered phenotypes. The traditional approach requires n(n − 2)/2 comparisons for n phenotypes. We present a direct method for determining a differentia network under multiple phenotypes. We explore the non-discrete nature of gene expression as a pattern in a fuzzy rough set. An edge between a pair of genes represents a positive region of a fuzzy similarity relationship upon a phenotypic change. We apply a weight-ranking formula and obtain a directed ranked network; we label this as a phenotype interwoven network. Those nodes with large in-degree connectivity bubble up as significant genes under respective phenotypic changes. We tested the method on six diseases and achieved good corroboration with the results of previous studies in the two-step approach. The subgraphs of the isolated genes achieved good significance upon validation through an information theoretic approach. The top-ranking genes determined in all of our case studies are in consonance with the findings of the respective wet-lab tests.