Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies.

Xu, Jinling; Zhou, Hui; Cheng, Yangyang; Xiang, Guangda

Xu, Jinling; Zhou, Hui; Cheng, Yangyang; Xiang, Guangda.

Afiliação

Xu J; The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515 Guangdong China.
Zhou H; Department of Endocrinology, General Hospital of Central Theater Command, Wuhan, 430070 Hubei China.
Cheng Y; Department of General Surgery, Central South University, The Third Xiangya Hospital, Changsha, 410013 Hunan China.
Xiang G; The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515 Guangdong China.

EPMA J ; 13(3): 433-449, 2022 Sep.

Article em En | MEDLINE | ID: mdl-36061826

Palavras-chave

Atherosclerosis; Biomarkers; DHRS9; Integrative analysis; Machine-learning strategy; Macrophage; Predictive preventive personalized medicine (PPPM)

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: EPMA J Ano de publicação: 2022 Tipo de documento: Article País de publicação: Suíça

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