The hypothesis of this study has been that a module of disease-associated genes could be found and validated in allergic disease, and then used to find and validate one or more novel disease genes.
If the prioritized genes of a queried phenotype are further divided into sub-groups which are related to subtypes of the disease, then we call each sub-group of genes as the susceptible modules of disease subtypes.
Since disease genes tend to interact with each other [ 1, 2] the choice may be facilitated by searching for modules of disease genes that are co-expressed and interact [ 3, 4].
However, the enrichment analysis implemented in the method is based on differentially expressed genes, disease driver genes are not clearly defined, so that drugs targeting the causal dysfunctional signaling modules of disease maybe missed out (Lee et al., 2012).
In this study, we propose a novel computational tool, DrugComboRanker, to prioritize drug combinations for specific cancers by selecting combinations targeting the alternative and complementary signaling modules of disease.
The algorithm assigns a distance value to candidate genes based on a random walk with restart (RWR), which reflects how well candidate genes are connected to modules of disease genes [ 56].
Since higher network scores signify a smaller global distance to the set of input disease genes, this demonstrates that addition of genes that were predicted on the basis of disease/gene associations supported the network scores of test genes in the vicinity of modules of disease genes.
This is not only due to the fact that they are naturally available from various databases, but also due to their capability to describe the module characteristic of disease genes.
We used two state-of-art network clustering approaches [ 25, 26] to identify the modules of diseases-drugs.
