From our GS BC module construction, GSs (or BCs) are clustered together based on their co-expression pattern across all the ROIs in the whole brain (or across all the genes in the genome).
First, the downstream targets of several well-known cancer-related genes such as MITF, TP53 and APC are retrieved from our association modules.
If a module generated this way did not satisfy our module density criterion (Q≥0.66), we extracted a subset of nodes from the module that met this criterion as the final module.
Apart from just analysing our modules in terms of this metric we compared their connectivity density with artificial modules.
Our modules pull from numerous advances made in systems biology, genomics (investigating the function and structure of genes and genomes), and phylogenetics (identifying and understanding evolutionary relationships among the various kinds of life on earth), facilitated by the use of mathematical and computational methods.
Both the genes from our identified network modules and those miRNA regulators are found to be indeed related to the development of oral cancers, indicating the important roles of these modules and their miRNA regulators in the pathogenesis of oral cancer.
We extracted eight modules (designated module A to module H) from our network, each represented by a group of genes that share similar expression patterns.
The total power loss is about 32%% for module 1 and 27 % for module 2. From our experiments we find that a complete failure of the PV-modules would have occurred if tephra fall would have accumulated coarse ash >1.6 kg/m2, or if fine ash would have exceeded 0.8 kg/m2 (Fig. 5).
This module from our framework deals with the various feature sets under consideration and analyzes them in order to obtain insights from it.
For visualization purposes, we selected those genes positively correlated with each module from our samples and carried out hierarchical clustering.
