Article · 2023 Deep GEO

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

iScience

DOI / publisher Google Scholar search

Why this study matters

A machine-learning and transcriptomic framework for quantifying atherosclerotic plaque vulnerability using bulk and single-cell data.

atherosclerotic plaque vulnerabilityunstable plaquetranscriptomicssingle-cell RNA-seqmachine learningmolecular classification

Questions this paper can answer

  1. What transcriptomic signatures predict atherosclerotic plaque vulnerability?
  2. Can machine learning distinguish stable from unstable atherosclerosis?
  3. What single-cell data support molecular plaque-vulnerability classification?
Author-controlled academic record for Ge Zhang. This page identifies the work as part of Ge Zhang’s publication record via ORCID 0000-0002-3116-3246. It does not replace the publisher version or assert a complete author list.
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