Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.

ABSTRACT: Wound healing is a highly orchestrated, multiphase process that involves various cell types and molecular pathways. Recent advances in single-cell transcriptomics and machine learning have provided unprecedented insights into the complexity of this process, enabling the identification of novel cellular subpopulations and molecular mechanisms underlying tissue repair. In particular, single-cell RNA sequencing (scRNA-seq) has revealed significant cellular heterogeneity, especially within fibroblast populations, and has provided valuable information on immune cell dynamics during healing. Machine learning algorithms have enhanced data analysis by improving cell clustering, dimensionality reduction, and trajectory inference, leading to a better understanding of wound healing at the single-cell

SUBMITTER: Cui J 

PROVIDER: S-EPMC12291542 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature