Wang Bo, Wu Peilong, Du Xiaoxin, Zhang Chunyu, Fu Shanshan, Sun Tang, Yang Xue
School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China; Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, Heilongjiang 161006, China.
School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China; Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, Heilongjiang 161006, China.
Comput Biol Chem. 2025 Jun 30;119:108581. doi: 10.1016/j.compbiolchem.2025.108581.
Microbes play a crucial role in the onset, progression, and treatment of diseases. To address the challenges of missing information and insufficient feature fusion in microbe-disease association prediction, this paper proposes an innovative computational model named MVGCVAE. MVGCVAE is the first model to synergistically integrate multi-view graph convolutional networks (GCNs), variational autoencoders (VAEs), and dynamic kernel matrix weighting for microbe-disease association (MDA) prediction. First, we construct multiple similarity networks between microbes and diseases, using GCN to independently process the node features in each view. To better fuse information from different similarity views, we introduce an attention mechanism to assign different weights to each perspective, thereby generating an initial comprehensive feature representation of diseases and microbes. This enables the model to more effectively integrate features from various perspectives and enhances its sensitivity and discriminative ability for key features. Next, based on a heterogeneous network, we feed the fused node features into the GCN for further representation learning. After each layer of feature extraction, we use a Variational Autoencoder (VAE) for variational inference to optimize node representations and enhance adaptation to sparse data and nonlinear relationships. Then, we propose a dynamic weighted kernel matrix strategy. This strategy uses a multi-layer perceptron (MLP) to adaptively generate weights, flexibly integrating kernel matrices computed from different embeddings at each layer to optimize the feature fusion process. Finally, we combine the weighted matrix with the feature matrix using matrix multiplication to calculate the microbe-disease association, and further optimize the model's predictive capability through Laplacian Regularization. Experimental results show that MVGCVAE outperforms six existing comparison methods on multiple evaluation metrics. Additionally, case studies further validate the reliability of MVGCVAE in predictive tasks.
微生物在疾病的发生、发展和治疗中起着至关重要的作用。为了应对微生物 - 疾病关联预测中信息缺失和特征融合不足的挑战，本文提出了一种名为MVGCVAE的创新计算模型。MVGCVAE是首个将多视图图卷积网络（GCN）、变分自编码器（VAE）和动态核矩阵加权协同集成用于微生物 - 疾病关联（MDA）预测的模型。首先，我们构建微生物和疾病之间的多个相似性网络，使用GCN独立处理每个视图中的节点特征。为了更好地融合来自不同相似性视图的信息，我们引入注意力机制为每个视角分配不同权重，从而生成疾病和微生物的初始综合特征表示。这使模型能够更有效地整合来自各个视角的特征，并增强其对关键特征的敏感性和判别能力。接下来，基于异构网络，我们将融合后的节点特征输入到GCN中进行进一步的表示学习。在每一层特征提取之后，我们使用变分自编码器（VAE）进行变分推理，以优化节点表示并增强对稀疏数据和非线性关系的适应性。然后，我们提出一种动态加权核矩阵策略。该策略使用多层感知器（MLP）自适应生成权重，灵活地整合从每一层不同嵌入计算得到的核矩阵，以优化特征融合过程。最后，我们使用矩阵乘法将加权矩阵与特征矩阵相结合来计算微生物 - 疾病关联，并通过拉普拉斯正则化进一步优化模型的预测能力。实验结果表明，MVGCVAE在多个评估指标上优于六种现有的比较方法。此外，案例研究进一步验证了MVGCVAE在预测任务中的可靠性。
