Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability.

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 biomark

SUBMITTER: Zhang G 

PROVIDER: S-EPMC10470306 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature