To their surprise, the scientists discovered 48 modules shared by plants and people.
However, few studies attempt to discover the mutual gene modules shared by the drug and disease association.
How to analyze transcriptional response data to identify meaningful gene modules shared by both drugs and diseases is still a critical issue for drug-disease associations and molecular mechanism.
The degree to which two reaction nodes remain together was assessed by the partition score Hij, which scales the number of modules shared by both reaction nodes with respect to the total depth of the partitions for each reaction node (see Methods for definition of depth).
We start by defining and detecting consensus modules, i.e., modules that are shared by two or more gene co-expression networks.
In the multiple cases analyses, after filtering by single case module, the variations shared by disease samples are selected and those present in control samples (if they are submitted) are excluded from further analysis.
First, we describe several methods for detecting modules that are shared by two or more networks (referred to as consensus modules).
This was done by ranking all modules from most to least responsive to CR (or aging) (i.e., rank modules by M), and discarding any module sharing at least one gene with any more responsive module of higher rank.
Third, bioinformatic comparisons of upstream regulatory regions identified predicted transcription factor binding site modules that are shared by genes expressed in the same trophoblast subtype.
We evaluated the predicted node assignments obtained by the methods against the known assignments, in which most nodes are assigned to exactly one module with probability 1, and those shared by two modules belong to each with probability 0.5.
