  Motivation: Identifying the molecular pathways more prone to disruption
during a pathological process is a key task in network medicine and, more in
general, in systems biology.
  Results: In this work we propose a pipeline that couples a machine learning
solution for molecular profiling with a recent network comparison method. The
pipeline can identify changes occurring between specific sub-modules of
networks built in a case-control biomarker study, discriminating key groups of
genes whose interactions are modified by an underlying condition. The proposal
is independent from the classification algorithm used. Three applications on
genomewide data are presented regarding children susceptibility to air
pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's.
  Availability: Details about the software used for the experiments discussed
in this paper are provided in the Appendix.
