HyboWaveNet: Hyperbolic Graph Neural Networks with Multi-Scale Wavelet Transform for Protein-Protein Interaction Prediction (2504.20102v1)

Abstract: 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

• The paper introduces HyboWaveNet, a novel deep learning framework integrating hyperbolic graph neural networks and multi-scale wavelet transforms for enhanced protein-protein interaction prediction.
• HyboWaveNet leverages hyperbolic geometry to model complex hierarchical protein structures and uses multi-scale wavelet transforms for adaptive feature extraction at different resolutions.
• Experiments demonstrate HyboWaveNet achieves state-of-the-art performance on public datasets, with AUC 0.922 and AUPR 0.938, highlighting its improved predictive accuracy over existing methods.

Overview of HyboWaveNet for Protein-Protein Interaction Prediction

The research paper titled "HyboWaveNet: Hyperbolic Graph Neural Networks with Multi-Scale Wavelet Transform for Protein-Protein Interaction Prediction" introduces a novel approach to predicting protein-protein interactions (PPIs). PPIs play a crucial role in understanding cellular functions, disease mechanisms, and drug discovery. While traditional machine learning methods have been effective in PPI prediction, their ability to capture complex hierarchical relationships and dynamic interaction patterns among proteins is limited. The paper addresses these limitations by proposing HyboWaveNet, a deep learning framework that integrates hyperbolic graph neural networks (HGNNs) with multiscale wavelet transforms.

Core Methodology

HyboWaveNet is built on the principles of geometric deep learning, specifically leveraging hyperbolic geometry to enhance the representation of node features. The hyperbolic space allows for modeling hierarchical structures inherent in biological systems more effectively than Euclidean space. This is achieved by mapping protein features into Lorentz space, thus simulating topological relationships among proteins via hyperbolic distances. The Lorentz space model facilitates more accurate capture of the proteins' scale-free distribution and complex hierarchical structures.

The framework also incorporates a multi-scale graph wavelet transform, which is pivotal for adaptive feature extraction across different resolutions. This mechanism captures both local and global interaction features, essential for understanding diverse biological phenomena. The multi-scale approach allows for the analysis of biological networks by revealing interaction patterns at varying levels of granularity.

Experimental Findings

The paper demonstrates the efficacy of HyboWaveNet through rigorous experiments on publicly available PPI datasets. The results indicate that HyboWaveNet outperforms several state-of-the-art methods in PPI prediction. Specifically, the model achieved an area under the ROC curve (AUC) of 0.922 and an area under the precision-recall curve (AUPR) of 0.938. Such outcomes highlight the improved predictive performance and generalization capability of HyboWaveNet compared to existing methods.

Ablation studies further validate the contributions of the multi-scale wavelet transform and the hyperbolic network architecture. When the encoder is changed from Lorentz space to traditional GCNs under Euclidean space, predictive performance notably diminishes, underscoring the advantages of hyperbolic embedding in capturing complex network dynamics.

Implications and Future Directions

The research provides a significant advancement in PPI prediction by linking geometric deep learning with signal processing techniques. This integrated approach offers a principled method for analyzing complex biological systems. The findings can facilitate further exploration in bioinformatics, specifically in areas demanding high-accuracy modeling of hierarchical structures.

Moving forward, a potential direction for future research could involve the exploration of additional geometric spaces for embedding biological data, or expansion of HyboWaveNet's applications beyond PPI prediction to other areas such as gene regulation or metabolic pathways. Furthermore, integrating additional biological insights into the model could improve its applicability and accuracy in diverse biological contexts.

In conclusion, HyboWaveNet exemplifies how combining advanced geometric learning methodologies with multiscale signal analysis can significantly enhance the robustness and accuracy of molecular interaction predictions, thus contributing meaningfully to computational biology and bioinformatics research.