Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers.

ABSTRACT:

Background

To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU).

Methods

Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the

SUBMITTER: Yu X 

PROVIDER: S-EPMC11562697 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature