ArticleBMC ophthalmology2023
Identification of potential biomarkers of myopia based on machine learning algorithms.
Article in BMC ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- DNA methylation biomarkers and myopia: a multi-omics study integrating GWAS, mQTL and eQTL data.Clinical epigenetics · 2024Pooled it
- Article
- Comprehensive bioinformatics analysis of metabolism‑related microRNAs in high myopia in young and old adults with age‑related cataracts.Molecular medicine reports · 2025Article
- Biomarkers: Promising Tools Towards the Diagnosis, Prognosis, and Treatment of Myopia.Journal of clinical medicine · 2024Review
- Research Progress on the Role of Ubiquitination in Eye Diseases.Cell biochemistry and biophysics · 2024Review
- Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork.Journal of animal science · 2024Article
- Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.The Journal of international medical research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims to identify potential myopia biomarkers using machine learning algorithms, enhancing myopia diagnosis and prognosis prediction.
methodsGSE112155 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.
resultsThe functional analysis of differentially expressed genes indicated their role in carbohydrate generation and polysaccharide synthesis. We identified 23 differentially expressed genes associated with myopia, four of which were highly effective diagnostic biomarkers. Single gene GSEA results showed these genes control the ubiquitin-mediated protein hydrolysis pathway.
conclusionOur study identifies four key myopia biomarkers, providing a foundation for future clinical and experimental validation studies.
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Registered trials
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