Journal of Indian Society of Periodontology · 2025
Graph attention network predicts drug-gene associations of matrix metalloproteinases 9-based host modulation in periodontitis.
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Arumuganainar Deepavalli, Anegundi Raghavendra Vamsi, Ganesh P R, Yadalam Pradeep Kumar
Аннотация
INTRODUCTION: Matrix metalloproteinases (MMPs) are essential endopeptidases involved in matrix degradation and remodeling, including periodontal tissues. They are classified into collagenases, gelatinases, stromelysin, matrilysin, and membrane types. MMPs, particularly MMP-2 and 9, contribute to gingival tissue breakdown in periodontitis. The study uses Graph Attention Network (GAT) to predict drug-gene associations for MMP-9 in host modulation, a crucial aspect of disease diagnosis, prognosis, targeted therapies, personalized medicine, and mechanistic studies. This approach can optimize treatment outcomes and minimize side effects, contributing to precision medicine.
MATERIALS AND METHODS: Data on drugs and genes associated with MMP-9 were retrieved using probes and drugs, and 1898 drug-gene interactions were studied. Data were cleaned for missing values, and graph data were prepared using nodes, gene names, and edges. Edge weights represented biochemical activity, while node features provided additional details for training a GAT. Cytoscape was used to create a network graph for drug-gene associations, while Cytohubba applied the maximum clique centrality algorithm to a drug-gene interaction network. A GAT model, consisting of three layers, was applied using Google Colab in a Python environment.
RESULTS: The network graph has 742 nodes, 1897 edges, and an average number of neighbors of 5.049. It has a characteristic path length of 3.303, with low local connectivity, and sparseness. The top-ten hubs with drug-gene associations with MMP-9 include quercetin, luteolin, econazole, zinc chloride, curcumin, MMP-9, MMP2, MMP1, MMP13, and MMP3. The model faces issues due to a dataset imbalance, with 80% of positive cases overfitting the majority class. Despite this, it learns useful features from the graph structure and shows stable training. The GAT model achieved an accuracy of 0.7955, indicating 80% correct classification, and an F1 score of 0.8861.
CONCLUSION: This study explores the intricate relationship between drugs, genes, and MMP-9, using a GAT tool to identify potential drug targets. Addressing limitations can advance MMP-9 biology and develop new therapeutic strategies.
MATERIALS AND METHODS: Data on drugs and genes associated with MMP-9 were retrieved using probes and drugs, and 1898 drug-gene interactions were studied. Data were cleaned for missing values, and graph data were prepared using nodes, gene names, and edges. Edge weights represented biochemical activity, while node features provided additional details for training a GAT. Cytoscape was used to create a network graph for drug-gene associations, while Cytohubba applied the maximum clique centrality algorithm to a drug-gene interaction network. A GAT model, consisting of three layers, was applied using Google Colab in a Python environment.
RESULTS: The network graph has 742 nodes, 1897 edges, and an average number of neighbors of 5.049. It has a characteristic path length of 3.303, with low local connectivity, and sparseness. The top-ten hubs with drug-gene associations with MMP-9 include quercetin, luteolin, econazole, zinc chloride, curcumin, MMP-9, MMP2, MMP1, MMP13, and MMP3. The model faces issues due to a dataset imbalance, with 80% of positive cases overfitting the majority class. Despite this, it learns useful features from the graph structure and shows stable training. The GAT model achieved an accuracy of 0.7955, indicating 80% correct classification, and an F1 score of 0.8861.
CONCLUSION: This study explores the intricate relationship between drugs, genes, and MMP-9, using a GAT tool to identify potential drug targets. Addressing limitations can advance MMP-9 biology and develop new therapeutic strategies.