Before we began to identify the modules, we examined the distribution patterns of gene expression values.
For a finer-grained look at the participants' experiences with the learning modules, we examined the distributions of responses on the individual scale items (descriptively only).
To better understand the relationships between modules, we examined the correlations between eigengenes of each module, and found a very strong negative correlation between the 'blue' and 'turquoise' module eigengenes (correlation = −0.90; Additional file 1: Figure S2).
To better understand the biological functions of identified regulatory modules, we examined them at the systems level by looking for interaction partners for predicted miRNA targets in a large protein-interaction network.
To investigate the prognostic potential of critical nodes and modules, we examined the ability of their corresponding genes to distinguish between poor and good outcomes after MI (specified below).
The module we examine is one of negative feedback leading to oscillations (studied in [ 28]).
In this module, we examine the similarities and the differences between some of these models, including a minimal differential equations model of the lac operon (Santillán and Mackey, 2008) and a PDS counterpart based on the same wiring diagram.
To further explore whether other chemical signals in addition to ROS (H2O2) were also involved in the activation of the putative ROS-bZIP – as1/ocs regulatory module, we examined the effect of exogenous ABA on the expression of representative subset of H2O2-inducible genes by northern blot.
In this review, we examine functional modules and their assemblies in RNAi processes.
We examine this module separately, as it is of interest to examine whether this distinct kind of switching behaviour yields results which are different from the other monostable switch and has been used in modelling [ 59, 60].
