用双曲图神经网络+多尺度小波分析，提升蛋白质相互作用预测准确率。
HyboWaveNet: Hyperbolic Graph Neural Networks with Multi-Scale Wavelet Transform for Protein-Protein Interaction Prediction
- 将蛋白质特征映射到双曲空间，模拟生物分子的层级拓扑关系。
- 多尺度小波变换有效捕捉局部与全局交互模式，提升预测性能。
- 适合研究生物网络、复杂系统建模的学者使用。
蛋白质-蛋白质相互作用（PPI）在解析细胞功能、疾病通路和药物发现中至关重要。尽管现有深度学习方法在PPI预测上已达到高精度，但其黑箱特性导致结果缺乏因果解释，且难以捕捉蛋白质间的层级几何结构与多尺度动态交互模式。为此，我们提出HyboWaveNet，一种融合双曲图神经网络（HGNNs）与多尺度图小波变换的新框架，实现稳健的PPI预测。通过将蛋白特征映射至Lorentz空间，利用双曲距离度量模拟生物分子的层级拓扑关系，使节点特征表示更符合生物学先验。HyboWaveNet天然建模层级与无标度生物关系，结合小波变换可自适应提取不同分辨率下的局部与全局交互特征。框架基于Lorenz模型进行图神经网络特征表示生成，并在多个视角下生成正样本对用于对比学习，再经多尺度图小波变换进一步提取特征以预测潜在PPI。在公开数据集上的实验表明，HyboWaveNet优于现有最先进方法。消融实验也验证了多尺度图小波变换模块显著提升了模型的预测性能与泛化能力。本工作连接几何深度学习与信号处理，推动了复杂生物系统的分析方法发展。
原文摘要 · Abstract (English)
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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