Jiang Cong, Feng Junxuan, Shan Bingshen, Chen Qiyue, Yang Jian, Wang Gang, Peng Xiaogang, Li Xiaozheng
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China.
Front Microbiol. 2024 Dec 13;15:1483983. doi: 10.3389/fmicb.2024.1483983. eCollection 2024.
In the contemporary field of life sciences, researchers have gradually recognized the critical role of microbes in maintaining human health. However, traditional biological experimental methods for validating the association between microbes and diseases are both time-consuming and costly. Therefore, developing effective computational methods to predict potential associations between microbes and diseases is an important and urgent task. In this study, we propose a novel computational framework, called GCATCMDA, for forecasting potential associations between microbes and diseases. Firstly, we construct Gaussian kernel similarity networks for microbes and diseases using known microbe-disease association data. Then, we design a feature encoder that combines graph convolutional network and graph attention mechanism to learn the node features of networks, and propose a feature dual-fusion module to effectively integrate node features from each layer's output. Next, we apply the feature encoder separately to the microbe similarity network, disease similarity network, and microbe-disease association network, and enhance the consistency of features for the same nodes across different association networks through contrastive learning. Finally, we pass the microbe and disease features into an inner product decoder to obtain the association scores between them. Experimental results demonstrate that the GCATCMDA model achieves superior predictive performance compared to previous methods. Furthermore, case studies confirm that GCATCMDA is an effective tool for predicting microbe-disease associations in real situations.
在当代生命科学领域，研究人员逐渐认识到微生物在维持人类健康方面的关键作用。然而，用于验证微生物与疾病之间关联的传统生物学实验方法既耗时又昂贵。因此，开发有效的计算方法来预测微生物与疾病之间的潜在关联是一项重要且紧迫的任务。在本研究中，我们提出了一种名为GCATCMDA的新型计算框架，用于预测微生物与疾病之间的潜在关联。首先，我们利用已知的微生物-疾病关联数据构建微生物和疾病的高斯核相似性网络。然后，我们设计了一种结合图卷积网络和图注意力机制的特征编码器来学习网络的节点特征，并提出了一个特征双融合模块来有效整合各层输出的节点特征。接下来，我们将特征编码器分别应用于微生物相似性网络、疾病相似性网络和微生物-疾病关联网络，并通过对比学习增强不同关联网络中相同节点的特征一致性。最后，我们将微生物和疾病特征输入到内积解码器中以获得它们之间的关联分数。实验结果表明，与先前的方法相比，GCATCMDA模型具有卓越的预测性能。此外，案例研究证实GCATCMDA是一种在实际情况下预测微生物-疾病关联的有效工具。
