Grammatical evolution support vector machines for predicting human genetic disease association

Authors:
Skylar Marvel;Alison Motsinger-Reif
Affiliations:
North Carolina State University, Raleigh, NC 27695, USA;North Carolina State University, Raleigh, NC 27695, USA
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

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Abstract

Identifying genes that predict common, complex human diseases is a major goal of human genetics. This is made difficult by the effect of epistatic interactions and the need to analyze datasets with high-dimensional feature spaces. Many classification methods have been applied to this problem, one of the more recent being Support Vector Machines (SVM). Selection of which features to include in the SVM model and what parameters or kernels to use can often be a difficult task. This work uses Grammatical Evolution (GE) as a way to choose features and parameters. Initial results look promising and encourage further development and testing of this new approach.