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ACIIDS
2010
IEEE

Evolving Concurrent Petri Net Models of Epistasis

9 years 16 days ago
Evolving Concurrent Petri Net Models of Epistasis
Abstract. A 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.
Michael Mayo, Lorenzo Beretta
Added 29 Sep 2010
Updated 29 Sep 2010
Type Conference
Year 2010
Where ACIIDS
Authors Michael Mayo, Lorenzo Beretta
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