图形
卷积(计算机科学)
计算机科学
频道(广播)
滤波器(信号处理)
算法
理论计算机科学
数学
人工智能
计算机网络
计算机视觉
人工神经网络
作者
Zhen Zhang,Zhaohong Deng,Ruibo Li,Te Zhang,Qiongdan Lou,Kup‐Sze Choi,Shitong Wang
标识
DOI:10.1109/tcbb.2024.3434399
摘要
Predicting biomolecular interactions is significant for understanding biological systems. Most existing methods for link prediction are based on graph convolution. Although graph convolution methods are advantageous in extracting structure information of biomolecular interactions, two key challenges still remain. One is how to consider both the immediate and highorder neighbors. Another is how to reduce noise when aggregating high-order neighbors. To address these challenges, we propose a novel method, called mixed high-order graph convolution with filter network via LSTM and channel attention (HGLA), to predict biomolecular interactions. Firstly, the basic and high-order features are extracted respectively through the traditional graph convolutional network (GCN) and the two-layer Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (MixHop). Secondly, these features are mixed and input into the filter network composed of LayerNorm, SENet and LSTM to generate filtered features, which are concatenated and used for link prediction. The advantages of HGLA are: 1) HGLA processes high-order features separately, rather than simply concatenating them; 2) HGLA better balances the basic features and high-order features; 3) HGLA effectively filters the noise from high-order neighbors. It outperforms state-ofthe-art networks on four benchmark datasets. The codes are available at https://github.com/zznb123/HGLA.
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