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      Learning Petri net models of non-linear gene interactions

      Mayo, Michael
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      Learning Petri Net Models of NL Genes.pdf
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      DOI
       10.1016/j.biosystems.2005.06.002
      Link
       www.sciencedirect.com
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      Mayo M. (2005). Learning Petri net models of non-linear gene interactions. BioSystems, 82(1), 74-82.
      Permanent Research Commons link: https://hdl.handle.net/10289/2175
      Abstract
      Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or “explanation” of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene–gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene–gene interactions recently reported in the literature.
      Date
      2005
      Type
      Journal Article
      Publisher
      Elsevier Science Publishers B.V.
      Rights
      This is the accepted manuscript version of an article published in the journal: BioSystems, © Copyright 2005 Elsevier B.V.
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      • Computing and Mathematical Sciences Papers [1443]
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