Defining protein complexes in the cell is important for learning about cellular processes mechanisms as they perform many of the molecular functions in these processes. Most of the proposed algorithms predict a complex as a dense area in a Protein–Protein Interaction (PPI) network. Others, on the other hand, weight the network using gene expression or geneontology (GO). These approaches, however, eliminate the proteins and their edges that offer no gene expression data. This can lead to the loss of important topological relations. Therefore, in this study, a method based on the Gene Expression and Core-Attachment (GECA) approach was proposed for addressing these limitations. GECA is a new technique to identify core proteins using common neighbor techniques and biological information. Moreover, GECA improves the attachment technique by adding the proteins that have low closeness but high similarity to the gene expression of the core proteins. GECA has been compared with several existing methods and proved in most datasets to be able to achieve the highest F-measure. The evaluation of complexes predicted by GECA shows high biological significance.

中文翻译：

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基于基因表达谱和核心附着方法从蛋白质-蛋白质相互作用网络中识别蛋白质复合物

定义细胞中的蛋白质复合物对于了解细胞过程机制很重要，因为它们在这些过程中执行许多分子功能。大多数提出的算法都将复合物预测为蛋白质-蛋白质相互作用 (PPI) 网络中的密集区域。另一方面，其他人则使用基因表达或基因学 (GO) 对网络进行加权。然而，这些方法消除了不提供基因表达数据的蛋白质及其边缘。这可能导致丢失重要的拓扑关系。因此，在本研究中，提出了一种基于基因表达和核心附着 (GECA) 方法的方法来解决这些限制。GECA 是一种使用常见的相邻技术和生物信息来识别核心蛋白的新技术。而且，GECA 通过添加与核心蛋白的基因表达具有低接近性但高相似性的蛋白质来改进附着技术。GECA 已与几种现有方法进行了比较，并在大多数数据集中证明能够实现最高的 F 度量。GECA 预测的复合物的评估显示出很高的生物学意义。