Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach.

Liu, Yan; Gao, Yuzhao; Fang, Ruiling; Cao, Hongyan; Sa, Jian; Wang, Jianrong; Liu, Hongqi; Wang, Tong; Cui, Yuehua

Liu, Yan; Gao, Yuzhao; Fang, Ruiling; Cao, Hongyan; Sa, Jian; Wang, Jianrong; Liu, Hongqi; Wang, Tong; Cui, Yuehua.

Afiliação

Liu Y; Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.
Gao Y; School of Statistics, Shanxi University of Finance and Economics, Taiyuan, PR China.
Fang R; Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.
Cao H; Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.
Sa J; Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.
Wang J; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA.
Liu H; Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.
Wang T; Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China.
Cui Y; Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA.

Brief Bioinform ; 22(6)2021 11 05.

Article em En | MEDLINE | ID: mdl-34373892

Assuntos

Simulação por Computador; Epistasia Genética; Algoritmos; Humanos; Fenótipo

Palavras-chave

Genegene interaction; high-dimensional testing; kernel function; linear interaction; nonlinear interaction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Simulação por Computador / Epistasia Genética Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article

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