ArticleiScience2023
Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
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- Machine learning combined with omics-based approaches reveals T-lymphocyte cellular fate imbalance in abdominal aortic aneurysm.BMC biology · 2025Article
- Deciphering the Proteomic Landscape of Circulating Extracellular Vesicles in Human Abdominal Aortic Aneurysm.Journal of cellular and molecular medicine · 2025Article
- Allostatic load-cardiovascular disease associations and the mediating effect of inflammatory factors: a prospective cohort study.Frontiers in cardiovascular medicine · 2025Article
- Differential Expression Spectrum of circRNA in Plasma Exosomes in Dilated Cardiomyopathy With Heart Failure.Journal of cellular and molecular medicine · 2024Article
- Assessing personalized molecular portraits underlying endothelial-to-mesenchymal transition within pulmonary arterial hypertension.Molecular medicine (Cambridge, Mass.) · 2024Article
- AI hybrid survival assessment for advanced heart failure patients with renal dysfunction.Nature communications · 2024Article
- Molecular subtypes of ischemic heart disease based on circadian rhythm.Scientific reports · 2024Article
- An assessment system for clinical and biological interpretability in ulcerative colitis.Aging · 2024Article
- Quantifying Personalized Shift-Work Molecular Portraits Underlying Alzheimer's Disease through Computational Biology.The journal of prevention of Alzheimer's disease · 2024Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.
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