Deep Neural Networks Based on Sp7 Protein Sequence Prediction in Peri‐Implant Bone Formation

<b>Objective:</b> Peri-implant bone regeneration is crucial for dental implant success, particularly in managing peri-implantitis, which causes inflammation and bone loss. SP7 (Osterix) is vital for osteoblast differentiation and bone matrix formation. Advances in deep neural networks (DNNs) offer new ways to analyze protein sequences, potentially improving our understanding of SP7's role in bone formation. This study aims to develop and utilize DNNs to predict the SP7 protein sequence and understand its role in peri-implant bone formation. <b>Materials:</b> and Methods: Sequences were retrieved from UniProt IDs Q8TDD2 and Q9V3Z2 using the UniProt dataset. The sequences were Sp7 fasta sequences. These sequences were located, and their quality was assessed. We built an architecture that can handle a wide range of input sequences using a DNN technique, with computing needs based on the length of the input sequences. <b>Results:</b> Protein sequences were analyzed using a DNN architecture with ADAM optimizer over 50 epochs, achieving a sensitivity of 0.89 and a specificity of 0.82. The receiver operating characteristic (ROC) curve demonstrated high true-positive rates and low false-positive rates, indicating robust model performance. Precision-recall analysis underscored the model's effectiveness in handling imbalanced data, with significant area under the curve (AUC-PR). Epoch plots highlighted consistent model accuracy throughout training, confirming its reliability for protein sequence analysis. <b>Conclusion:</b> The DNN employed with ADAM optimizer demonstrated robust performance in analyzing protein sequences, achieving an accuracy of 0.85 and high sensitivity and specificity. The ROC curve highlighted the model's effectiveness in distinguishing true positives from false positives, which is essential for reliable protein classification. These findings suggest that the developed model is promising for enhancing predictive capabilities in computational biology and biomedical research, particularly in protein function prediction and therapeutic development applications.