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.

Afiliación

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 en 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.

Asunto(s)

Metilación de ADN; Epigénesis Genética; Humanos; Epigenoma; Ciencia de los Datos; Epigenómica

Palabras clave

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

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Metilación de ADN / Epigénesis Genética Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Epigenomics Año: 2024 Tipo del documento: Article País de afiliación: Japón

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