Deep Learning Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane Vesicles.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39221175", "kind": "science-record", "revision": 3, "content_hash": "sha256:2ca4921dd69f4c0117e9c4dc3c7a257b9acb3686b7343cef6258bcdbcc6284ae", "created_at": "2026-11-01T20:29:21Z", "updated_at": "2026-11-01T22:12:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deep Learning Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane Vesicles.", "doi": "10.1177/11795972241277081", "pmid": "39221175", "authors": ["Pradeep Kumar Yadalam", "Raghavendra Vamsi Anegundi", "Muthupandian Saravanan", "Hadush Negash Meles", "Artak Heboyan"], "journal": "Biomedical engineering and computational biology", "year": 2024, "abstract": "Aim: The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences.\nMaterial and methods: The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation.\nResults: Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, F1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship.\nConclusion: In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs.", "claims": [{"id": "claim-method", "statement": "The Kruskal-Wallis H-test was used to compare the distributions of model metrics (AUC, Classification Accuracy, F1 Score, Precision, Recall, and Specificity) across Neural Networks, Naive Bayes, and Gradient Boosting.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39221175", "locator": "sections[3] \"Results\" paragraph 6", "quote": "The Kruskal-Wallis H-test was used to compare the distributions of model metrics (AUC, Classification Accuracy, F1 Score, Precision, Recall, and Specificity) across Neural Networks, Naive Bayes, and Gradient Boosting."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Using the GEO R tool on the NCBI GEO DATA SET GSE218606, the most differentially expressed mRNA was identified from outer membrane vesicles of P. gingivalis (Figures 1 and 2).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39221175", "locator": "sections[2] \"Material and Methods\" paragraph 2", "quote": "Using the GEO R tool on the NCBI GEO DATA SET GSE218606, the most differentially expressed mRNA was identified from outer membrane vesicles of P. gingivalis (Figures 1 and 2)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The Kruskal-Wallis H-test found no statistically significant difference in model metrics among Neural Networks, Naive Bayes, and Gradient Boosting (H-statistic 2.000 for all metrics; P-values .368).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39221175", "locator": "sections[3] \"Results\" paragraph 6", "quote": "The results show that the H-statistic is 2.000 for all metrics, with corresponding P-values of .368."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The Kruskal-Wallis H-test found no statistically significant difference in model metrics among Neural Networks, Naive Bayes, and Gradient Boosting (H-statistic 2.000 for all metrics; P-values .368).", "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:39221175"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE218606"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"auc": "ated using metrics like Area Under the Curve (AUC), Classification A"}, "inputs": ["data:geo:GSE218606"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39221175", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1177/11795972241277081", "sha256": "sha256:ea737d3ce194dab6dcd22136817d7686a390a6e729c94f7d5510f58ba10b42fc", "bytes": 19399, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1177/11795972241277081", "version": null, "note": "Full article fetched from PMC; 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