ArticleInternational dental journal2025
Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation.
Article in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integrative Network Pharmacology and Molecular Docking-Based Validation of Berberine as a Therapeutic Agent in Arsenic-Induced Cardiotoxicity.Cardiovascular toxicology · 2026Article
- AI-based prediction of drug-gene interactions modulating tight junction integrity: A deep learning framework highlighting multiple therapeutic targets.Journal of oral biology and craniofacial researchArticle
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4 authors.
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Abstract
INTRODUCTION AND
objectivesOral diseases, including gingivitis and periodontitis, are linked to the Wnt signaling pathway, vital for bone metabolism, cementum homeostasis, and mesenchymal stem cell differentiation. Advances in generative AI techniques, such as variational autoencoders (VAEs) and quantum variational classifiers (QVCs), offer promising tools for predicting gene associations between drugs and diseases. This study aims to compare the predictive performance of VAEs and QVCs in modeling drug-disease gene networks within the Wnt signaling pathway in periodontal inflammation.
methodsGenes associated with Wnt-related periodontal inflammation were identified through comprehensive literature reviews and genomic databases. Their roles in various biological processes were evaluated using gene set enrichment analysis, employing tools like Enrichr, which integrates diverse gene sets from sources such as DSigDB, DisGeNET, and Lincs_l1000.drug. The study then applied VAEs and QVCs to predict gene-disease associations related to the Wnt signaling pathway.
resultsThe analysis revealed an extensive network comprising 1738 nodes and 1498 edges, averaging 1.992 neighbors per node. The network exhibited a diameter of 2, a radius of 1, and a characteristic path length of 1.992, indicating limited interconnectivity. The VQA model demonstrated a high accuracy rate of 97.5%, although it only detected 50% of anomalies. The VQC model achieved a precision of 78%, with Class 1 samples showing improved recall and a balanced F1 score.
conclusionVQC and VAE models exhibit strong potential for discovering FDA-approved drugs by predicting gene-drug associations in periodontitis based on the Wnt signaling pathway. CLINICAL RELEVANCE: This study highlights the potential of VAEs and QVCs in predicting gene-drug associations for periodontal inflammation. This could lead to more targeted therapies for oral diseases like periodontitis, improving patient outcomes and advancing personalized treatment strategies in clinical practice.
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