Open AccessAssessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms
Author(s) -
Nicholas S. Roetker,
David L. Page,
James A. Yonker,
Virginia W. Chang,
Carol Roan,
Pamela Herd,
Taissa S. Hauser,
Robert M. Hauser,
Craig S. Atwood
Publication year - 2013
Publication title -
american journal of public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.284
H-Index - 264
eISSN - 1541-0048
pISSN - 0090-0036
DOI - 10.2105/ajph.2012.301141
Subject(s) - logistic regression , psychology , machine learning , clinical psychology , population stratification , single nucleotide polymorphism , computer science , biology , genetics , genotype , gene
We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms.