title: Machine learning approaches for the discovery of gene-gene interactions in disease data.
abstract: Because of the complexity of gene-phenotype relationships machine learning approaches have considerable appeal as a strategy for modelling interactions. A number of such methods have been developed and applied in recent years with some modest success. Progress is hampered by the challenges presented by the complexity of the disease genetic data, including phenotypic and genetic heterogeneity, polygenic forms of inheritance and variable penetrance, combined with the analytical and computational issues arising from the enormous number of potential interactions. We review here recent and current approaches focusing, wherever possible, on applications to real data (particularly in the context of genome-wide association studies) and looking ahead to the further challenges posed by next generation sequencing data.
authors.affiliation: University of Southampton, UK.
authors.forename: Rosanna
authors.initials: R
authors.lastname: Upstill-Goddard
authors.forename: Diana
authors.initials: D
authors.lastname: Eccles
authors.forename: Joerg
authors.initials: J
authors.lastname: Fliege
authors.forename: Andrew
authors.initials: A
authors.lastname: Collins
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D057225:Data Mining; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006801:Humans; D020412:Multifactorial Inheritance; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide; D060388:Support Vector Machine