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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37740201", "kind": "science-record", "revision": 4, "content_hash": "sha256:0a147b5db6de058eaf7a87b970a321af289f5bccd6db397097727b3abcee984f", "created_at": "2026-11-01T19:02:34Z", "updated_at": "2026-11-01T22:52:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential biomarkers of myopia based on machine learning algorithms.", "doi": "10.1186/s12886-023-03119-5", "pmid": "37740201", "authors": ["Shengnan Zhang", "Tao Wang", "Huaihua Wang", "Bingfang Gao", "Chao Sun"], "journal": "BMC ophthalmology", "year": 2023, "abstract": "PURPOSE: This study aims to identify potential myopia biomarkers using machine learning algorithms, enhancing myopia diagnosis and prognosis prediction.\nMETHODS: 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.\nRESULTS: The 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.\nCONCLUSION: Our study identifies four key myopia biomarkers, providing a foundation for future clinical and experimental validation studies.", "claims": [{"id": "claim-method", "statement": "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.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37740201", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "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."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The available data has been placed in the Supplementary materials.Web links and URLs: The GEO database (https://www.ncbi.nlm.nih.gov/geo/).The GSEA database (http://www.gsea-msigdb.org/gsea/index.jsp).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37740201", "locator": "sections[24] \"notes\" paragraph 1", "quote": "The available data has been placed in the Supplementary materials.Web links and URLs: The GEO database (https://www.ncbi.nlm.nih.gov/geo/).The GSEA database (http://www.gsea-msigdb.org/gsea/index.jsp)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified 23 differentially expressed genes associated with myopia, four of which were highly effective diagnostic biomarkers.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37740201", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified 23 differentially expressed genes associated with myopia, four of which were highly effective diagnostic biomarkers."}, {"asset": "source:pmid:37740201", "locator": "sections[13] \"Diagnostic efficiency of biomarkers\" paragraph 2", "quote": "NR1D1 (AUC = 0.986) in GSE151631, PGBD2 (AUC = 1.000), PPP1R3D (AUC = 1.000), PPP1R18 (AUC = 1.000) (Fig. 5A); NR1D1 (AUC = 0.810), PGBD2 (AUC = 0.710), PPP1R3D (AUC = 0.800), in GSE112155 PPP1R18 (AUC = 0.720) (Fig. 5B). This result indicates that all four biomarkers have good diagnostic efficiency."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified 23 differentially expressed genes associated with myopia, four of which were highly effective diagnostic biomarkers.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37740201"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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