Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson's Diseases and Improving the Disease Classification Using Support Vector Machine.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35342758", "kind": "science-record", "revision": 5, "content_hash": "sha256:c3d16b907d6ab645414d8b0eaf5137761f8e831f64aef153056c54c776c2bf33", "created_at": "2026-11-01T18:14:08Z", "updated_at": "2026-11-01T22:48:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson's Diseases and Improving the Disease Classification Using Support Vector Machine.", "doi": "10.1155/2022/5009892", "pmid": "35342758", "authors": ["Shadi Moradi", "Leili Tapak", "Saeid Afshar"], "journal": "BioMed research international", "year": 2022, "abstract": "Background: Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine-producing neurons was the main cause of this disease. The purpose of this research was to discover genes involved in PD development.\nMethods: In this study, the microarray dataset (GSE22491) provided by GEO was used for further analysis. The Limma package under R software was used to examine and assess gene expression and identify DEGs. The DAVID online tool was used to accomplish GO enrichment analysis and KEGG pathway for DEGs. Furthermore, the PPI network of these DEGs was depicted using the STRING database and analyzed through the Cytoscape to identify hub genes. Support vector machine (SVM) classifier was subsequently employed to predict the accuracy of genes.\nResult: PPI network consisted of 264 nodes as well as 502 edges was generated using the DEGs recognized from the Limma package under the R software. Moreover, three genes were identified as hubs: GNB5, GNG11, and ELANE. By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved.\nConclusion: According to the findings of the study, the 3 hub genes GNB5, GNG11, and ELANE may be used as PD detection biomarkers. Moreover, the results obtained from SVM with high accuracy can be considered as PD biomarkers in further investigations.", "claims": [{"id": "claim-method", "statement": "An iterative process based on the SVM technique was used to find the relevant genes with the highest sensitivity, specificity, and accuracy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35342758", "locator": "sections[12] \"3.3. Statistical Analysis for Determining Sensitivity, Specificity, and Accuracy\" paragraph 2", "quote": "An iterative process based on the SVM technique was used to find the relevant genes with the highest sensitivity, specificity, and accuracy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The expression profile data used to support the findings of this study have been deposited in the GEO database (GSE22491 and GSE54536 ).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35342758", "locator": "sections[15] \"Data Availability\" paragraph 2", "quote": "The expression profile data used to support the findings of this study have been deposited in the GEO database (GSE22491 and GSE54536 )."}], "state": "complete"}, {"id": "claim-target-result", "statement": "By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved.", "kind": "quantitative", "target": {"value": 88.0, "unit": "per cent", "tolerance": null, "source": "By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved."}, "evidence": [{"asset": "source:pmid:35342758", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "By using 3-gene combination, SVM found that prediction accuracy of 88% can be achieved.", "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:35342758"], "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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