Protein complexes are the foundation of all cellular activities, and accurately identifying them is crucial for studying cellular systems. The efficient discovery of protein complexes is a focus of research in the field of bioinformatics. Most existing methods for protein complex identification are based on the structure of the protein-protein interaction (PPI) network, whereas some methods attempt to integrate biological information to enhance the features of the protein network for complex identification. Existing protein complex identification methods are unable to fully integrate network topology information and biological attribute information. Most of these methods are based on homogeneous networks and cannot distinguish the importance of different attributes and protein nodes. To address these issues, a GO attribute Heterogeneous Attention network Embedding (GHAE) method based on heterogeneous protein information networks is proposed. First, GHAE incorporates Gene Ontology (GO) information into the PPI network, constructing a heterogeneous protein information network. Then, GHAE uses a dual attention mechanism and heterogeneous graph convolutional representation learning method to learn protein features and to identify protein complexes. The experimental results show that building heterogeneous protein information networks can fully integrate valuable biological information. The heterogeneous graph embedding learning method can simultaneously mine the features of protein and GO attributes, thereby improving the performance of protein complex identification.

中文翻译：

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基于异质蛋白质信息网络的蛋白质复合物鉴定。

蛋白质复合物是所有细胞活动的基础，准确识别它们对于研究细胞系统至关重要。蛋白质复合物的高效发现是生物信息学领域的研究热点。现有的蛋白质复合物识别方法大多基于蛋白质-蛋白质相互作用（PPI）网络的结构，而一些方法试图整合生物信息来增强蛋白质网络的特征以进行复合物识别。现有的蛋白质复合物识别方法无法充分整合网络拓扑信息和生物属性信息。这些方法大多基于同质网络，无法区分不同属性和蛋白质节点的重要性。针对这些问题，提出了一种基于异构蛋白质信息网络的GO属性异构注意力网络嵌入（GHAE）方法。首先，GHAE将基因本体（GO）信息纳入PPI网络，构建异构蛋白质信息网络。然后，GHAE使用双重注意力机制和异构图卷积表示学习方法来学习蛋白质特征并识别蛋白质复合物。实验结果表明，构建异质蛋白质信息网络可以充分整合有价值的生物信息。异构图嵌入学习方法可以同时挖掘蛋白质特征和GO属性，从而提高蛋白质复合体识别的性能。