Yadalam Pradeep Kumar, Ramya R, Anegundi Raghavendra Vamsi, Chatterjee Shubhangini
Department of Periodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.
Department of Oral Biology, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.
Cureus. 2024 Sep 4;16(9):e68669. doi: 10.7759/cureus.68669. eCollection 2024 Sep.
Introduction The Wnt/β-catenin pathway is crucial for bone formation and remodeling, regulating osteoblast differentiation, bone remodeling, and skeletal homeostasis. Dysregulation of the Wnt/β-catenin pathway is linked to bone-related diseases like osteoporosis, osteoarthritis, and osteosarcoma. The strategies to modulate this pathway include Wnt agonists, inhibitors, and small molecules. Graph neural networks (GNNs) have shown potential in understanding drug-gene interactions, providing accurate predictions, identifying novel drug-target pairs, and enabling personalized drug therapy. So we aim to predict GNN-based drug-gene interactions of Wnt/β-catenin pathway in bone formation. Methodology The drug-gene interactions of Wnt signaling were annotated and preprocessed using Cytoscape, a powerful tool for building drug-gene interactions. Data was imported, nodes representing drugs and genes were created, and edges represented their interactions. GNNs were used to prepare data for nodes, genes, and drugs. GNNs are designed to operate on graph-structured data, capable of learning complex relationships between the nodes. The architecture consists of several steps: graph representation, message passing, node representation update, graph-level readout, and prediction or output. A data representation system is a GNN with an Adam optimizer, 100 epochs, a learning rate of 0.001, and entropy loss. Results The network has 108 nodes, 134 edges, and 2.444 neighbors, with a diameter of 4, radius of 2, and characteristic path length of 2.635. It lacks clustering, sparse connectivity, wide connection variation, and moderate centralization. The GNN model's drug-gene interactions demonstrate high precision, recall, F1 score, and accuracy, with a high sensitivity to true-positives and low false-negatives. Conclusion The study employs a GNN model to predict drug-gene interactions in the Wnt/β-catenin pathway, demonstrating high precision and accuracy, but further research is needed.
引言
Wnt/β-连环蛋白信号通路对于骨形成和重塑至关重要，可调节成骨细胞分化、骨重塑和骨骼稳态。Wnt/β-连环蛋白信号通路的失调与骨质疏松症、骨关节炎和骨肉瘤等骨相关疾病有关。调节该信号通路的策略包括Wnt激动剂、抑制剂和小分子。图神经网络（GNN）在理解药物-基因相互作用、提供准确预测、识别新型药物-靶点对以及实现个性化药物治疗方面已显示出潜力。因此，我们旨在预测基于GNN的Wnt/β-连环蛋白信号通路在骨形成中的药物-基因相互作用。
方法
使用Cytoscape（一种构建药物-基因相互作用的强大工具）对Wnt信号的药物-基因相互作用进行注释和预处理。导入数据，创建代表药物和基因的节点，边代表它们之间的相互作用。GNN用于为节点、基因和药物准备数据。GNN旨在对图结构数据进行操作，能够学习节点之间的复杂关系。其架构包括几个步骤：图表示、消息传递、节点表示更新、图级读出以及预测或输出。数据表示系统是一个具有Adam优化器、100个轮次、0.001学习率和熵损失的GNN。
结果
该网络有108个节点、134条边和2.444个邻居，直径为4，半径为2，特征路径长度为2.635。它缺乏聚类、稀疏连接性、广泛的连接变化和适度的中心性。GNN模型的药物-基因相互作用显示出高精度、召回率、F1分数和准确率，对真阳性具有高敏感性，对假阴性具有低敏感性。
结论
该研究采用GNN模型预测Wnt/β-连环蛋白信号通路中的药物-基因相互作用，显示出高精度和准确性，但仍需进一步研究。
