Data science using the human epigenome for predicting multifactorial diseases and symptoms.

Nishitani, Shota; Smith, Alicia K; Tomoda, Akemi; Fujisawa, Takashi X

Nishitani, Shota; Smith, Alicia K; Tomoda, Akemi; Fujisawa, Takashi X.

Afiliação

Nishitani S; Research Center for Child Mental Development, University of Fukui, Fukui, 910-1193, Japan.
Smith AK; Division of Developmental Higher Brain Functions, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, & University of Fukui, Osaka, 565-0871, Japan.
Tomoda A; Life Science Innovation Center, School of Medical Sciences, University of Fukui, Fukui, 910-8507, Japan.
Fujisawa TX; Gynecology & Obstetrics, Emory University School of Medicine, Atlanta, GA 30322, USA.

Epigenomics ; 16(5): 273-276, 2024 Mar.

Article em En | MEDLINE | ID: mdl-38312014

ABSTRACT

ABSTRACT

Tweetable abstract This article reviews machine learning models that leverages epigenomic data for predicting multifactorial diseases and symptoms as well as how such models can be utilized to explore new research questions.

Assuntos

Metilação de DNA; Epigênese Genética; Humanos; Epigenoma; Ciência de Dados; Epigenômica

Palavras-chave

DNA methylation; epigenetic epidemiology; epigenetic technologies; epigenetics; epigenetics and disease; epigenome-wide association studies

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Metilação de DNA / Epigênese Genética Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Epigenomics Ano de publicação: 2024 Tipo de documento: Article

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