Based on multiple machine learning to identify the ENO2 as diagnosis biomarkers of glaucoma.

ABSTRACT:

Purpose

Glaucoma is a generic term of a highly different disease group of optic neuropathies, which the leading cause of irreversible vision in the world. There are few biomarkers available for clinical prediction and diagnosis, and the diagnosis of patients is mostly delayed.

Methods

Differential gene expression of transcriptome sequencing data (GSE9944 and GSE2378) for normal samples and glaucoma samples from the GEO database were analyzed. Furthermore, based on different algorithms (Logistic Regression (LR), Random Forest (RF), lasso regression (LASSO)) two diagnostic models are constructed and diagnostic markers are screened. GO and KEGG analyses revealed the possible mechanism of differential genes in the pathogenesis of glaucoma. ROC curve confirmed the effectiveness.<

SUBMITTER: Dai M 

PROVIDER: S-EPMC8976990 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature