Show simple item record  

dc.contributor.authorMayo, Michael
dc.contributor.authorBeretta, Lorenzo
dc.date.accessioned2010-10-08T01:38:09Z
dc.date.available2010-10-08T01:38:09Z
dc.date.issued2010
dc.identifier.citationMayo, M., Beretta, L. (2010). Modelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset mining. Expert Systems with Applications, 38(4), 4006-4013.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/4669
dc.description.abstractPetri nets are useful for mathematically modelling disease-causing genetic epistasis. A Petri net model of an interaction has the potential to lead to biological insight into the cause of a genetic disease. However, defining a Petri net by hand for a particular interaction is extremely difficult because of the sheer complexity of the problem and degrees of freedom inherent in a Petri net’s architecture. We propose therefore a novel method, based on evolutionary computation and data mining, for automatically constructing Petri net models of non-linear gene interactions. The method comprises two main steps. Firstly, an initial partial Petri net is set up with several repeated sub-nets that model individual genes and a set of constraints, comprising relevant common sense and biological knowledge, is also defined. These constraints characterise the class of Petri nets that are desired. Secondly, this initial Petri net structure and the constraints are used as the input to a genetic algorithm. The genetic algorithm searches for a Petri net architecture that is both a superset of the initial net, and also conforms to all of the given constraints. The genetic algorithm evaluation function that we employ gives equal weighting to both the accuracy of the net and also its parsimony. We demonstrate our method using an epistatic model related to the presence of digital ulcers in systemic sclerosis patients that was recently reported in the literature. Our results show that although individual “perfect” Petri nets can frequently be discovered for this interaction, the true value of this approach lies in generating many different perfect nets, and applying data mining techniques to them in order to elucidate common and statistically significant patterns of interaction.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherElsevieren_NZ
dc.rightsThis is an author’s accepted version of an article published in the journal: Expert Systems with Applications. © 2010 Elsevier.en_NZ
dc.subjectPetri neten_NZ
dc.subjectgenetic algorithmen_NZ
dc.subjectfrequent itemset miningen_NZ
dc.subjectepistasisen_NZ
dc.subjectgenetic diseaseen_NZ
dc.subjectMachine learning
dc.titleModelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset miningen_NZ
dc.typeJournal Articleen_NZ
dc.identifier.doi10.1016/j.eswa.2010.09.062en_NZ
dc.relation.isPartOfExpert Systems with Applicationsen_NZ
pubs.begin-page4006en_NZ
pubs.elements-id35386
pubs.end-page4013en_NZ
pubs.issue4en_NZ
pubs.volume38en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record