  Profiling of whole transcriptomes has become a cornerstone of molecular
biology and an invaluable tool for the characterization of clinical phenotypes
and the identification of disease subtypes. Analyses of these data are becoming
ever more sophisticated as we move beyond simple comparisons to consider
networks of higher-order interactions and associations. Gene regulatory
networks model the regulatory relationships of transcription factors and genes
and have allowed the identification of differentially regulated processes in
disease systems. In this perspective we discuss gene targeting scores, which
measure changes in inferred regulatory network interactions, and their use in
identifying disease-relevant processes. In addition, we present an example
analysis or pancreatic ductal adenocarcinoma demonstrating the power of gene
targeting scores to identify differential processes between complex phenotypes;
processes which would have been missed by only performing differential
expression analysis. This example demonstrates that gene targeting scores are
an invaluable addition to gene expression analysis in the characterization of
diseases and other complex phenotypes.
