Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation
Introduction and Objectives: Oral 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 q...
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Elsevier
2025-02-01
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| Series: | International Dental Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0020653924015375 |
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| author | Pradeep Kumar Yadalam Prabhu Manickam Natarajan Musab Hamed Saeed Carlos M. Ardila |
| author_facet | Pradeep Kumar Yadalam Prabhu Manickam Natarajan Musab Hamed Saeed Carlos M. Ardila |
| author_sort | Pradeep Kumar Yadalam |
| collection | DOAJ |
| description | Introduction and Objectives: Oral 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. Methods: Genes 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. Results: The 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. Conclusion: VQC 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. |
| format | Article |
| id | doaj-art-21d1267ae58e4c9caeaf3276efa06bc5 |
| institution | Kabale University |
| issn | 0020-6539 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Dental Journal |
| spelling | doaj-art-21d1267ae58e4c9caeaf3276efa06bc52025-01-21T04:12:45ZengElsevierInternational Dental Journal0020-65392025-02-01751185194Variational Approaches for Drug-Disease-Gene Links in Periodontal InflammationPradeep Kumar Yadalam0Prabhu Manickam Natarajan1Musab Hamed Saeed2Carlos M. Ardila3Department of Periodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences,Saveetha University, Chennai 600077, Tamil Nadu, IndiaDepartment of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman University, Ajman, United Arab Emirates; Corresponding author. Department of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman University, PO Box: 346, Ajman, United Arab Emirates.Associate Professor, Restorative Dentistry, College of Dentistry. Ajman University, Ajman, United Arab EmiratesCarlos-M. Ardila. DDS. Periodontist. Ph.D in Epidemiology. Postdoc in Bioethics Titular Professor. Universidad de Antioquia U de A, Medellín, Colombia. Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín, ColombiaIntroduction and Objectives: Oral 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. Methods: Genes 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. Results: The 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. Conclusion: VQC 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.http://www.sciencedirect.com/science/article/pii/S0020653924015375periodontitisWnt Signaling Pathwaymachine learningquantum computingGene-Drug Interaction |
| spellingShingle | Pradeep Kumar Yadalam Prabhu Manickam Natarajan Musab Hamed Saeed Carlos M. Ardila Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation International Dental Journal periodontitis Wnt Signaling Pathway machine learning quantum computing Gene-Drug Interaction |
| title | Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation |
| title_full | Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation |
| title_fullStr | Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation |
| title_full_unstemmed | Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation |
| title_short | Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation |
| title_sort | variational approaches for drug disease gene links in periodontal inflammation |
| topic | periodontitis Wnt Signaling Pathway machine learning quantum computing Gene-Drug Interaction |
| url | http://www.sciencedirect.com/science/article/pii/S0020653924015375 |
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