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dc.contributor.authorMayo, Michael
dc.date.accessioned2009-05-21T21:22:01Z
dc.date.available2009-05-21T21:22:01Z
dc.date.issued2005
dc.identifier.citationMayo M. (2005). Learning Petri net models of non-linear gene interactions. BioSystems, 82(1), 74-82.en
dc.identifier.urihttps://hdl.handle.net/10289/2175
dc.description.abstractUnderstanding 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherElsevier Science Publishers B.V.en
dc.relation.urihttp://www.sciencedirect.com/science/journal/03032647en
dc.rightsThis is the accepted manuscript version of an article published in the journal: BioSystems, © Copyright 2005 Elsevier B.V.en
dc.subjectcomputer scienceen
dc.subjectEpistasisen
dc.subjectPetri net high order gene-gene interactionen
dc.subjectmulti-start random hill climbingen
dc.subjectMachine learning
dc.titleLearning Petri net models of non-linear gene interactionsen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.biosystems.2005.06.002en
dc.relation.isPartOfBioSystemsen_NZ
pubs.begin-page74en_NZ
pubs.elements-id31978
pubs.end-page82en_NZ
pubs.issue1en_NZ
pubs.volume82en_NZ


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