Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification.

ABSTRACT: Background: Keratoconus (KTCN) is a multifactorial disease characterized by progressive corneal degeneration. Recent studies suggest that a gene expression analysis of corneas may uncover potential novel biomarkers involved in corneal matrix remodeling. However, identifying reliable combinations of biomarkers that are linked to disease risk or progression remains a significant challenge. Objective: This study employed multiple machine learning algorithms to analyze the transcriptomes of keratoconus patients, identifying feature gene combinations and their functional associations, with the aim of enhancing the understanding of keratoconus pathogenesis. Methods: We analyzed the GSE77938 (PRJNA312169) dataset for differential gene expression (DGE) and performed gene set e

SUBMITTER: Chang SH 

PROVIDER: S-EPMC12109562 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature