Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39246530", "kind": "science-record", "revision": 4, "content_hash": "sha256:4f7c4747e54d7cbcf15f6f3ea912b646e3b4757f8d83f3f72783a506f130001e", "created_at": "2026-11-01T20:29:22Z", "updated_at": "2026-11-01T22:55:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.", "doi": "10.1177/11795972241277639", "pmid": "39246530", "authors": ["Pradeep Kumar Yadalam", "Raghavendra Vamsi Anegundi", "Sivasankari Thilagar", "Deepavalli Arumuganainar", "Deepti Shrivastava", "Artak Heboyan"], "journal": "Biomedical engineering and computational biology", "year": 2024, "abstract": "Background: The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway.\nMethods: Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering.\nResults: Random Forest emerged as the superior model, achieving the highest R 2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R 2 = .952, RMSE = 0.332), Linear Regression (R 2 = .949, RMSE = 0.343), and SVM (R 2 = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease.\nConclusion: The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.", "claims": [{"id": "claim-method", "statement": "Novel biomarkers can be predicted by artificial neural networks, SVM, random forest, and linear regression models, which were used with an orange tool to analyze the expression data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39246530", "locator": "sections[2] \"Methods\" paragraph 2", "quote": "Novel biomarkers can be predicted by artificial neural networks, SVM, random forest, and linear regression models, which were used with an orange tool to analyze the expression data."}], "state": "complete"}, {"id": "claim-data-access", "statement": "When comparing healthy PDLSC cells to LPS-induced PDLSCs, using NCBI GEO DATA SET GSE163489, 8 researchers observed that the most differentially expressed micro RNAs were distinct.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39246530", "locator": "sections[2] \"Methods\" paragraph 2", "quote": "When comparing healthy PDLSC cells to LPS-induced PDLSCs, using NCBI GEO DATA SET GSE163489, 8 researchers observed that the most differentially expressed micro RNAs were distinct."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Random Forest emerged as the superior model, achieving the highest R2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R2 = .952, RMSE = 0.332), Linear Regression (R2 = .949, RMSE = 0.343), and SVM (R2 = .931, RMSE = 0.398).", "kind": "quantitative", "target": {"value": 2.0, "unit": "correlation coefficient", "tolerance": null, "source": "Random Forest emerged as the superior model, achieving the highest R2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R2 = .952, RMSE = 0.332), Linear Regression (R2 = .949, RMSE = 0.343), and SVM (R2 = .931, RMSE = 0.398)."}, "evidence": [{"asset": "source:pmid:39246530", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Random Forest emerged as the superior model, achieving the highest R2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R2 = .952, RMSE = 0.332), Linear Regression (R2 = .949, RMSE = 0.343), and SVM (R2 = .931, RMSE = 0.398)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Random Forest emerged as the superior model, achieving the highest R2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R2 = .952, RMSE = 0.332), Linear Regression (R2 = .949, RMSE = 0.343), and SVM (R2 = .931, RMSE = 0.398).", "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:39246530"], "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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