QR-Code

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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Yujia Zhou, MD, Boyang Xiang, MD, Xiaoqin Yang, PhD, Yuxin Ren, MD, Xiaosong Gu, PhD, Xiang Zhou, PhD
Format: Artigo
Sprache:Inglês
Veröffentlicht: Elsevier 2025-03-01
Schriftenreihe:JACC: Advances
Schlagworte:
cardiometabolic disease
GWAS
metabolomics
PheWAS
unsupervised learning
Online-Zugang:http://www.sciencedirect.com/science/article/pii/S2772963X25000377
Tags: Tag hinzufügen
Keine Tags, Fügen Sie das erste Tag hinzu!

Ähnliche Einträge