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dc.contributor.authorMayo, Michael
dc.contributor.authorBeretta, Lorenzo
dc.coverage.spatialConference held at Hue City, Vietnamen_NZ
dc.date.accessioned2009-12-15T01:21:41Z
dc.date.available2009-12-15T01:21:41Z
dc.date.issued2010
dc.identifier.citationMayo, M. & Beretta. L. (2010). Evolving concurrent Petri net models of epistasis. In N.T. Nguyen, M.T. Le & J. Swiatek (Eds.), Intelligent Information and Database Systems: Second International Conference, ACIIDS, Hue City, Vietnam, March 24-26, 2010. Proceedings, Part II (Pp. 166-175). Berlin Heidelberg: Springer Verlag.en
dc.identifier.isbn3-642-12100-5
dc.identifier.isbn978-3-642-12100-5
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/10289/3492
dc.description.abstractA genetic algorithm is used to learn a non-deterministic Petri netbased model of non-linear gene interactions, or statistical epistasis. Petri nets are computational models of concurrent processes. However, often certain global assumptions (e.g. transition priorities) are required in order to convert a non-deterministic Petri net into a simpler deterministic model for easier analysis and evaluation. We show, by converting a Petri net into a set of state trees, that it is possible to both retain Petri net non-determinism (i.e. allowing local interactions only, thereby making the model more realistic), whilst also learning useful Petri nets with practical applications. Our Petri nets produce predictions of genetic disease risk assessments derived from clinical data that match with over 92% accuracy.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_NZ
dc.relation.urihttp://aciids2010.hueuni.edu.vn/en
dc.rightsThis is the author's accepted version of a paper published by Springer in the series Lecture Notes in Artificial Intelligence (LNCS/LNAI). The original publication is available at www.springerlink.com.en
dc.subjectcomputer scienceen
dc.subjectPetri neten
dc.subjectgenetic algorithmen
dc.subjectepistasisen
dc.subjectconcurrencyen
dc.subjectsystemic schlerosisen
dc.subjectdigital ulcersen
dc.subjectMachine learning
dc.titleEvolving concurrent Petri net models of epistasisen
dc.typeConference Contributionen
dc.identifier.doi10.1007/978-3-642-12101-2
dc.relation.isPartOfIntelligent Information and Database Systemsen_NZ
pubs.begin-page166en_NZ
pubs.elements-id19316
pubs.end-page175en_NZ
pubs.finish-date2010-03-26en_NZ
pubs.issuePART 2en_NZ
pubs.start-date2010-03-24en_NZ
pubs.volumeSecond International Conference, ACIIDS, Proceedings, Part II. Lecture Notes in Computer Science, Volume 0/2010, LNAI 5991en_NZ


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