HyboWaveNet：用于蛋白质-蛋白质相互作用预测的基于多尺度小波变换的双曲图神经网络
HyboWaveNet: Hyperbolic Graph Neural Networks with Multi-Scale Wavelet Transform for Protein-Protein Interaction Prediction
AI总结：
针对PPI预测缺乏因果解释及难以捕捉层次几何与多尺度动态模式的问题，提出结合双曲图神经网络与多尺度图小波变换的深度学习框架HyboWaveNet，在公开数据集上优于现有最先进方法。
摘要：
蛋白质-蛋白质相互作用（PPIs）对于破译细胞功能、疾病途径和药物发现至关重要。尽管现有的神经网络和机器学习方法在PPI预测中实现了高准确率，但其黑盒性质导致对预测结果缺乏因果解释，且难以捕捉蛋白质间的层次几何结构和多尺度动态相互作用模式。为了应对这些挑战，我们提出了HyboWaveNet，这是一种新型深度学习框架，结合双曲图神经网络（HGNNs）和多尺度图小波变换，用于稳健的PPI预测。将蛋白质特征映射到Lorentz空间，通过双曲距离度量模拟生物分子间的层次拓扑关系，使节点特征表示更符合生物学先验。HyboWaveNet本质上模拟了层次和无标度的生物关系，而小波变换的整合实现了跨不同分辨率对局部和全局相互作用特征的自适应提取。我们的框架在Lorentz模型下通过图神经网络生成节点特征表示，并在多个不同视图下生成正样本对以进行对比学习，随后通过多尺度图小波变换进一步提取特征以预测潜在的PPIs。在公开数据集上的实验表明，HyboWaveNet优于现有的最先进方法。我们还通过消融实验研究证明，多尺度图小波变换模块提升了HyboWaveNet的预测性能和泛化能力。这项工作将几何深度学习与信号处理联系起来以推进PPI预测，为分析复杂生物系统提供了一种有原则的方法。
英文摘要：
Protein-protein interactions (PPIs) are fundamental for deciphering cellular functions,disease pathways,and drug discovery.Although existing neural networks and machine learning methods have achieved high accuracy in PPI prediction,their black-box nature leads to a lack of causal interpretation of the prediction results and difficulty in capturing hierarchical geometries and multi-scale dynamic interaction patterns among proteins.To address these challenges, we propose HyboWaveNet,a novel deep learning framework that collaborates with hyperbolic graphical neural networks (HGNNs) and multiscale graphical wavelet transform for robust PPI prediction. Mapping protein features to Lorentz space simulates hierarchical topological relationships among biomolecules via a hyperbolic distance metric,enabling node feature representations that better fit biological a priori.HyboWaveNet inherently simulates hierarchical and scale-free biological relationships, while the integration of wavelet transforms enables adaptive extraction of local and global interaction features across different resolutions. Our framework generates node feature representations via a graph neural network under the Lorenz model and generates pairs of positive samples under multiple different views for comparative learning, followed by further feature extraction via multi-scale graph wavelet transforms to predict potential PPIs. Experiments on public datasets show that HyboWaveNet improves over both existing state-of-the-art methods. We also demonstrate through ablation experimental studies that the multi-scale graph wavelet transform module improves the predictive performance and generalization ability of HyboWaveNet. This work links geometric deep learning and signal processing to advance PPI prediction, providing a principled approach for analyzing complex biological systems
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