title: Design of artificial neural network and its applications to the analysis of alcoholism data.
abstract: Artificial neural networks were applied to the alcoholism data to reveal nonlinear relationships between intermediate phenotypes, marker identity-by-descent sharing, and the affection status. A variable number of hidden units were considered to achieve a balance between the minimal mean-squared error and over-fitting of the data. The predictability of the affection status based on intermediate phenotype information (event-related potential 300, monoamine oxidase, and gender) was 65% to 75%, and sensitivity/specificity ranged around 50% to 80%. The IBD approach succeeded in identifying the same marker as previous studies, but also found additional peaks.
authors.affiliation: Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA.
authors.forename: W
authors.initials: W
authors.lastname: Li
authors.forename: F
authors.initials: F
authors.lastname: Haghighi
authors.forename: C T
authors.initials: CT
authors.lastname: Falk
mesh_terms: D000437:Alcoholism / Q000453:epidemiology / Q000235:genetics*; D000465:Algorithms; D002874:Chromosome Mapping; D005820:Genetic Testing; D006801:Humans; D016571:Neural Networks, Computer; D010641:Phenotype; D012044:Regression Analysis; D012984:Software