  A bioinformatics platform is introduced aimed at identifying models of
disease-specific pathways, as well as a set of network measures that can
quantify changes in terms of global structure or single link disruptions.The
approach integrates a network comparison framework with machine learning
molecular profiling. <CA>The platform includes different tools combined in one
Open Source pipeline, supporting reproducibility of the analysis. We describe
here the computational pipeline and explore the main sources of variability
that can affect the results, namely the classifier, the feature
ranking/selection algorithm, the enrichment procedure, the inference method and
the networks comparison function.
  The proposed pipeline is tested on a microarray dataset of late stage
Parkinsons' Disease patients together with healty controls. Choosing different
machine learning approaches we get low pathway profiling overlapping in terms
of common enriched elements. Nevertheless, they identify different but equally
meaningful biological aspects of the same process, suggesting the integration
of information across different methods as the best overall strategy.
  All the elements of the proposed pipeline are available as Open Source
Software: availability details are provided in the main text.
