Identification of potential biomarkers of myopia based on machine learning algorithms.

ABSTRACT:

Purpose

This study aims to identify potential myopia biomarkers using machine learning algorithms, enhancing myopia diagnosis and prognosis prediction.

Methods

GSE112155 and GSE15163 datasets from the GEO database were analyzed. We used "limma" for differential expression analysis and "GO plot" and "clusterProfiler" for functional and pathway enrichment analyses. The LASSO and SVM-RFE algorithms were employed to screen myopia-related biomarkers, followed by ROC curve analysis for diagnostic performance evaluation. Single-gene GSEA enrichment analysis was executed using GSEA 4.1.0.

Results

The functional analysis of differentially expressed genes indicated their role in carbohydrate generation and polysaccharide synthesis. We identified 23 differentially expressed gene

SUBMITTER: Zhang S 

PROVIDER: S-EPMC10517464 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature