Discovering periodontitis biomarkers and therapeutic targets through bioinformatics and ensemble learning analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41044121", "kind": "science-record", "revision": 3, "content_hash": "sha256:4247f18032ae828015f3701dac734191f4f84ee63f6a8139d75fdd3642ebfc0a", "created_at": "2026-11-01T20:31:06Z", "updated_at": "2026-11-01T22:14:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Discovering periodontitis biomarkers and therapeutic targets through bioinformatics and ensemble learning analysis.", "doi": "10.1038/s41598-025-18017-7", "pmid": "41044121", "authors": ["Md Tanvir Hasan", "Md Rakibul Islam", "Nasser Raqe Alqhtani", "Ali Robaian", "Abdullah Saad Alqahtani", "Fawaz Alqahtani", "Abdulaziz Mohammed Alsakr", "Huda Abutayyem", "Sam Thomas Kuriadom", "Maher A L Shayeb", "Mohammad Khursheed Alam"], "journal": "Scientific reports", "year": 2025, "abstract": "Periodontitis, a prevalent inflammatory disease, leads to the progressive destruction of periodontal tissues and poses significant systemic health risks. Despite its widespread impact, the molecular mechanisms driving periodontitis remain poorly understood. This study integrates advanced ensemble machine learning models and bioinformatics approaches to elucidate the genetic basis of periodontitis. Using transcriptomic data from the gene expression omnibus (GEO) repository (GSE10334), we identified 21 common genes from bagging and boosting models, underscoring their critical role in disease pathophysiology. Protein-protein interaction (PPI) network analysis revealed hub genes (HNRNPC, TSR1, PLRG1, GOPC) with central roles in key biological pathways. Functional enrichment highlighted their involvement in actin filament regulation, immune response modulation, and RNA processing. Furthermore, mutation and copy number alteration (CNA) analyses revealed significant genetic diversity in these hub genes, particularly in diploid samples, with a high prevalence of missense and splice variants. Together, these findings advance our understanding of the molecular landscape of periodontitis, paving the way for novel biomarker discovery and targeted therapeutic strategies potentially leading to improved diagnostic and treatment approaches in periodontal care.", "claims": [{"id": "claim-method", "statement": "Ensemble machine learning models, including boosting and bagging approaches, were employed to classify the data and extract feature importance through cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41044121", "locator": "sections[2] \"Methodology\" paragraph 3", "quote": "Ensemble machine learning models, including boosting and bagging approaches, were employed to classify the data and extract feature importance through cross-validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In this study, we utilized publicly available transcriptomic data from the Gene Expression Omnibus (GEO) repository (accession number GSE10334) to identify significant genetic markers associated with periodontitis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41044121", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "In this study, we utilized publicly available transcriptomic data from the Gene Expression Omnibus (GEO) repository (accession number GSE10334) to identify significant genetic markers associated with periodontitis."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using GSE10334 transcriptomic data, bagging and boosting models identified 21 common genes associated with periodontitis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41044121", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Using transcriptomic data from the gene expression omnibus (GEO) repository (GSE10334), we identified 21 common genes from bagging and boosting models, underscoring their critical role in disease pathophysiology."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using GSE10334 transcriptomic data, bagging and boosting models identified 21 common genes associated with periodontitis.", "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:41044121"], "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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