School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.
Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, Jinan 250014, China.
Biomolecules. 2021 May 28;11(6):799. doi: 10.3390/biom11060799.
Protein-protein interactions (PPIs) play a key role in signal transduction and pharmacogenomics, and hence, accurate PPI prediction is crucial. Graph structures have received increasing attention owing to their outstanding performance in machine learning. In practice, PPIs can be expressed as a signed network (i.e., graph structure), wherein the nodes in the network represent proteins, and edges represent the interactions (positive or negative effects) of protein nodes. PPI predictions can be realized by predicting the links of the signed network; therefore, the use of gated graph attention for signed networks (SN-GGAT) is proposed herein. First, the concept of graph attention network (GAT) is applied to signed networks, in which "attention" represents the weight of neighbor nodes, and GAT updates the node features through the weighted aggregation of neighbor nodes. Then, the gating mechanism is defined and combined with the balance theory to obtain the high-order relations of protein nodes to improve the attention effect, making the attention mechanism follow the principle of "low-order high attention, high-order low attention, different signs opposite". PPIs are subsequently predicted on the Saccharomyces cerevisiae core dataset and the Human dataset. The test results demonstrate that the proposed method exhibits strong competitiveness.
蛋白质-蛋白质相互作用 (PPIs) 在信号转导和药物基因组学中起着关键作用，因此准确的 PPI 预测至关重要。由于在机器学习方面的出色表现，图结构受到了越来越多的关注。在实践中，PPIs 可以表示为有向网络（即图结构），其中网络中的节点表示蛋白质，边表示蛋白质节点的相互作用（正或负效应）。通过预测有向网络的链接，可以实现 PPI 预测；因此，本文提出了使用门控图注意有向网络 (SN-GGAT)。首先，将图注意网络 (GAT) 的概念应用于有向网络，其中“注意”表示邻居节点的权重，GAT 通过邻居节点的加权聚合来更新节点特征。然后，定义门控机制并结合平衡理论来获得蛋白质节点的高阶关系，以提高注意效果，使注意机制遵循“低阶高注意，高阶低注意，不同符号相反”的原则。随后在酿酒酵母核心数据集和人类数据集上进行了 PPI 预测。测试结果表明，所提出的方法具有很强的竞争力。
