title: Application of probabilistic neural network analysis to a disease with complex inheritance: the GAW11 simulated data.
abstract: A probabilistic neural network analysis was applied to the simulated GAW11 data. Six replicates drawn at random from one of the simulated populations were used to generate training and test vectors for pairs of siblings. The vectors incorporated two environmental indicators as well as identical-by-descent allele sharing scores from each of 300 genetic markers. The performance of a 'naïve' probabilistic neural network (PNN) was fair. However, by combining a traditional linkage analysis with a PNN which incorporated gene x environment interaction, the performance was considerably enhanced.
authors.affiliation: Sunnybrook and Women's College Health Sciences Centre, Toronto, Canada.
authors.forename: D M
authors.initials: DM
authors.lastname: Naimark
authors.forename: A D
authors.initials: AD
authors.lastname: Paterson
mesh_terms: D000483:Alleles; D016009:Chi-Square Distribution; D004777:Environment; D005819:Genetic Markers; D005820:Genetic Testing; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009679:Nuclear Family; D010641:Phenotype
chemical_list: D005819:Genetic Markers