Código QR (código de barras bidimensional)

Unsupervised Learning-Derived Complex Metabolic Signatures Refine Cardiometabolic Risk

Background: Cardiometabolic diseases have become a leading cause of morbidity and mortality globally. Nuclear magnetic resonance metabolomics represents a precise tool for assessing metabolic individuality. Objectives: This study aimed to use unsupervised learning to decode plasma metabolomic profil...

ver descrição completa

Na minha lista:
Principais autores: Yujia Zhou, MD, Boyang Xiang, MD, Xiaoqin Yang, PhD, Yuxin Ren, MD, Xiaosong Gu, PhD, Xiang Zhou, PhD
Formato: Artigo
Idioma:Inglês
Publicado em: Elsevier 2025-03-01
coleção:JACC: Advances
Assuntos:
cardiometabolic disease
GWAS
metabolomics
PheWAS
unsupervised learning
Acesso em linha:http://www.sciencedirect.com/science/article/pii/S2772963X25000377
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!

Registros relacionados