Risk prediction performance

Biomarker1 paper1 finding

In silico1

Upstream

1

Machine-learning

upin silico1

1 study

machine-learning improves risk prediction performance in silico (AUROC 0.601 to 0.738).
In silico
Pathway Modeling of Genomic and Tissue-Specific Transcriptomic Architecture Identifies Personalized Mechanisms of Atrial Fibrillation Risk · medRxiv · 31 Aug 2026 · Preprint

Latest

5

Zhonghua Liu Xing Bing Xue Za ZhiHumans19 Sep

[Association between novel high-density lipoprotein cholesterol metabolic phenotypes and all-cause and cardiovascular disease mortality in older adults in communities in Beijing]

Cardiovascular disease

European Journal of Preventive CardiologyHumans16 Sep

A multistate prediction model for hypertension and cardiovascular disease after pregnancy using routine care data in the Netherlands

Journal of Diabetes and Metabolic DisordersHumans9 Sep

Comparative predictive value of the frailty index combined with non-insulin-based insulin resistance indicators for cardio-renal-metabolic multimorbidity risk: a large nationwide prospective cohort study

medRxivIn silicoPreprint31 Aug

Pathway Modeling of Genomic and Tissue-Specific Transcriptomic Architecture Identifies Personalized Mechanisms of Atrial Fibrillation Risk

Atrial fibrillation

Frontiers in EndocrinologyHumans26 Aug

Associations between novel triglyceride glucose and adiposity-related indices and risk of metabolic dysfunction-associated steatotic liver disease: the mediating role of biological aging and evidence from the UK Biobank

Metabolic dysfunction-associated steatotic liver disease