Identification of Important Genes of Keratoconus and Construction of the Diagnostic Model.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36160033", "kind": "science-record", "revision": 5, "content_hash": "sha256:465cfca25f7da9706d18ef44de02529c5d64f10b00440229d606865cff8b91f5", "created_at": "2026-11-01T18:15:27Z", "updated_at": "2026-11-01T22:50:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Important Genes of Keratoconus and Construction of the Diagnostic Model.", "doi": "10.1155/2022/5878460", "pmid": "36160033", "authors": ["Lin Wang", "Yuqing Wang", "Juan Liu", "Wencheng Zhao"], "journal": "Genetics research", "year": 2022, "abstract": "Objective: The aim of the study is to investigate the potential role of keratoconus (KC) in the diagnosis of keratoconus (KC).\nMethods: GSE151631 and GSE77938 were downloaded from the comprehensive gene expression database (GEO). By using the random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM), important immune-related genes were identified as biomarkers for KC diagnosis.\nResults: Through the LASSO, RFE, and RF algorithms and comparing the three sets of DEGs, a total of 8 overlapping DEGs were obtained. We took 8 DEGs as the final optimal combination of DEGs: AREG, BBC3, DUSP2, map3k8, Smad7, CDKN1A, JUN, and LIF.\nConclusion: Abnormal cell proliferation, apoptosis, and autophagy defects are related to KC, which may be the etiology and potential target of KC.", "claims": [{"id": "claim-method", "statement": "We adopted SVM (support vector machine, SVM) methods of R3.6.1 e1071 version 1.6–8 (https://cran.r-project.org/web/packages/e1071) and constructed an SVM classifier based on optimal feature gene combination (Core: sigmoid kernel; cross: 100 fold cross-validation).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36160033", "locator": "sections[9] \"2.7. Construction and Verification of the SVM Diagnostic Model\" paragraph 2", "quote": "We adopted SVM (support vector machine, SVM) methods of R3.6.1 e1071 version 1.6–8 (https://cran.r-project.org/web/packages/e1071) and constructed an SVM classifier based on optimal feature gene combination (Core: sigmoid kernel; cross: 100 fold cross-validation)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets (GSE151631, GSE77938, and GSE112155) used are available from the comprehensive gene expression database (GEO) at the NCBI GEO (https://www.ncbi.nlm.nih.gov/GEO/) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36160033", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "The datasets (GSE151631, GSE77938, and GSE112155) used are available from the comprehensive gene expression database (GEO) at the NCBI GEO (https://www.ncbi.nlm.nih.gov/GEO/) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We took 8 DEGs as the final optimal combination of DEGs: AREG, BBC3, DUSP2, map3k8, Smad7, CDKN1A, JUN, and LIF.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36160033", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We took 8 DEGs as the final optimal combination of DEGs: AREG, BBC3, DUSP2, map3k8, Smad7, CDKN1A, JUN, and LIF."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We took 8 DEGs as the final optimal combination of DEGs: AREG, BBC3, DUSP2, map3k8, Smad7, CDKN1A, JUN, and LIF.", "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:36160033"], "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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