Assessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms | Zendy

Nicholas S. Roetker | Zendy; C. David Page | Zendy; James A. Yonker | Zendy; Vicky C. Chang | Zendy; Carol Roan | Zendy; Pamela Herd | Zendy; Taissa S. Hauser | Zendy; Robert M. Hauser | Zendy; Craig Atwood | Zendy

Open Access

Assessment 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,

C. David Page,

James A. Yonker,

Vicky C. Chang,

Carol Roan,

Pamela Herd,

Taissa S. Hauser,

Robert M. Hauser,

Craig 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.

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