A Machine Learning Pipeline for Discriminant Pathways Identification

  • Annalisa Barla
  • Giuseppe Jurman
  • Roberto Visintainer
  • Margherita Squillario
  • Michele Filosi
  • Samantha Riccadonna
  • Cesare Furlanello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7548)


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. 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. Two applications on genomewide data are presented regarding children susceptibility to air pollution and early and late onset of Parkinson’s disease.


Pathway identification network comparison functional characterization profiling 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Annalisa Barla
    • 1
  • Giuseppe Jurman
    • 2
  • Roberto Visintainer
    • 2
    • 3
  • Margherita Squillario
    • 1
  • Michele Filosi
    • 2
    • 4
  • Samantha Riccadonna
    • 2
  • Cesare Furlanello
    • 2
  1. 1.DISIUniversity of GenoaGenovaItaly
  2. 2.Fondazione Bruno KesslerPovoItaly
  3. 3.DISIUniversity of TrentoPovoItaly
  4. 4.Centre for Integrative Biology (CIBIO)University of TrentoMattarelloItaly

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