⚡ Quick Summary
This study utilized a generative AI approach to predict hub genes involved in pulpal inflammation and regeneration, achieving a predictive accuracy of 76.92%. The findings highlight the potential of autoencoders in identifying therapeutic targets for enhancing endodontic treatment outcomes.
🔍 Key Details
- 📊 Dataset: Data from accession number GSE255672
- 🧩 Features used: Transcriptomic data, proinflammatory cytokines, NF-κB signaling
- ⚙️ Technology: Autoencoders, Cytoscape, CytoHubba
- 🏆 Performance: Accuracy 76.92%, Precision-Recall AUC 0.9214, ROC AUC 0.7333
🔑 Key Takeaways
- 🔬 Pulpal inflammation is critical for successful endodontic treatments.
- 🤖 Generative AI was employed to predict hub genes related to pulpal health.
- 📈 Autoencoders demonstrated moderate predictive performance in gene classification.
- 🏅 The model achieved an accuracy of 76.92%, indicating a balanced performance.
- 🌟 Precision-recall AUC of 0.9214 suggests strong identification of positive cases.
- 🔄 Further optimization is needed for improved predictive capabilities.
- 💡 Insights gained could lead to personalized strategies for pulpal health improvement.
- 📅 Published in: Sci Rep, 2025; 15:26225.
📚 Background
Pulpal inflammation and regeneration are essential processes that significantly influence the outcomes of endodontic treatments. Understanding the underlying biological mechanisms, particularly the roles of proinflammatory cytokines and stem cell activity, is crucial for developing effective therapeutic strategies. Recent advancements in transcriptomic studies have opened new avenues for exploring these complex interactions.
🗒️ Study
The study aimed to predict and reconstruct hub genes associated with pulpal inflammation and regeneration using a generative AI approach. Researchers performed differential gene expression analysis on a dataset from the GEO database, employing tools like Cytoscape and its plugin CytoHubba to construct a protein-protein interaction network and identify key genes.
📈 Results
The autoencoder-based model demonstrated a moderate predictive performance, achieving an accuracy of 76.92%. The precision-recall AUC of 0.9214 indicates a strong ability to identify positive cases, while the ROC AUC of 0.7333 reflects a reasonable correlation between predicted and actual classifications. These results underscore the potential of autoencoders in gene prediction tasks.
🌍 Impact and Implications
The findings from this study could significantly impact the field of endodontics by providing insights into the biological mechanisms underlying pulpal inflammation and regeneration. By identifying hub genes, researchers can develop personalized therapeutic strategies aimed at improving pulpal health, ultimately enhancing treatment outcomes for patients.
🔮 Conclusion
This study highlights the promising role of autoencoders in predicting hub genes related to pulpal inflammation and regeneration. The moderate predictive accuracy achieved suggests that with further optimization, these models could become valuable tools in the field of endodontics. Continued research in this area is essential for advancing our understanding and treatment of pulpal health issues.
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Prediction of hub genes in pulpal inflammation and regeneration using autoencoders and a generative AI approach.
Abstract
Pulpal inflammation and regeneration are crucial for enhancing endodontic treatment outcomes. Transcriptomic studies highlight the involvement of proinflammatory cytokines, NF-κB signaling, and stem cell activity. This study employs a generative AI approach to predict and reconstruct hub genes associated with these processes, providing insights into biological mechanisms and potential therapeutic targets. Differential gene expression analysis was performed on data from the accession number GSE255672 using the GEO2R tool and Cytoscape, a bioinformatics software platform. A protein-protein interaction network was constructed using gene ontology annotations to identify key genes and subnetworks. CytoHubba, a Cytoscape plugin, was used to pinpoint hub genes using the Maximal Clique Centrality method. The Dataset was normalized, cleaned, and categorized into hub and non-hub genes. The data was then split into 80% training and 20% test sets for analysis using autoencoders. Autoencoders, which reduce complex data into simplified feature sets, were employed to compress the data for classifier training. An autoencoder-based model was trained using the preprocessed dataset, demonstrating moderate predictive performance with an accuracy of 76.92%, a precision-recall AUC of 0.9214, and a ROC AUC of 0.7333. The model performed well, achieving good predictive accuracy. The autoencoder achieved an accuracy rate of 76.92%, indicating a balanced performance between precision and recall. The model exhibited strong performance in identifying positive cases, with an area under the precision-recall curve of 0.9214. While the model demonstrated a moderate correlation between predicted and actual classifications, there remains room for further optimization. This study demonstrates the potential utility of autoencoders in predicting hub genes involved in pulpal inflammation and regeneration. These findings aim to support personalized strategies for improving pulpal health.
Author: [‘Yadalam PK’, ‘Krithikadatta J’, ‘Natarajan PM’, ‘Ardila CM’]
Journal: Sci Rep
Citation: Yadalam PK, et al. Prediction of hub genes in pulpal inflammation and regeneration using autoencoders and a generative AI approach. Prediction of hub genes in pulpal inflammation and regeneration using autoencoders and a generative AI approach. 2025; 15:26225. doi: 10.1038/s41598-025-12074-8