Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis - an integrated bioinformatic approach.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39659491", "kind": "science-record", "revision": 4, "content_hash": "sha256:3820adb8cc2e74855c22a0cdbfd71065cdb6eef7736578100ad0ee0c2bf94f54", "created_at": "2026-11-01T20:29:57Z", "updated_at": "2026-11-01T22:55:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis - an integrated bioinformatic approach.", "doi": "10.3389/froh.2024.1462845", "pmid": "39659491", "authors": ["Pradeep Kumar Yadalam", "Sarvagya Sharma", "Prabhu Manickam Natarajan", "Carlos M Ardila"], "journal": "Frontiers in oral health", "year": 2024, "abstract": "Introduction: Peri-implantitis, a destructive inflammatory condition affecting the tissues surrounding dental implants, shares pathological similarities with periodontitis, a chronic inflammatory disease that impacts the supporting structures of natural teeth. This study utilizes a network-based approach to classify interactome hub genes associated with peri-implantitis and periodontitis, aiming to improve understanding of disease mechanisms and identify potential therapeutic targets.\nMethods: We employed gradient boosting and Weighted Gene Co-expression Network Analysis (WGCNA) to predict and classify these interactome hub genes. Gene expression data related to these diseases were sourced from the NCBI GEO dataset GSE223924, and differential gene expression analysis was conducted using the NCBI GEO R tool. Through WGCNA, we constructed a co-expression network to identify key hub genes, while gradient boosting was used to predict these hub genes.\nResults: Our analysis revealed a co-expression network comprising 216 genes, including prominent hub genes such as IL17RC, CCN2, BMP7, TPM1, and TIMP1, which are implicated in periodontal disease. The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis.\nDiscussion: These identified genes play roles in inflammation, osteoclast genesis, angiogenesis, and immune response regulation. This study highlights essential hub genes and molecular pathways associated with peri-implantitis and periodontitis, suggesting potential therapeutic targets for developing innovative treatment strategies.", "claims": [{"id": "claim-method", "statement": "The Gradient Boosting model with CatBoost architecture was configured with a learning rate of 0.009, comprising 26 trees, a regularization lambda of 0.05, and a limit of 5 individual trees.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39659491", "locator": "sections[11] \"Gradient boosting architecture\" paragraph 2", "quote": "The Gradient Boosting model with CatBoost architecture was configured with a learning rate of 0.009, comprising 26 trees, a regularization lambda of 0.05, and a limit of 5 individual trees."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39659491", "locator": "sections[17] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis.", "kind": "quantitative", "target": {"value": 88.2, "unit": "per cent", "tolerance": null, "source": "The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis."}, "evidence": [{"asset": "source:pmid:39659491", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and 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:39659491"], "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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