An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II . It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html .

中文翻译：

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一种改进的 3 合 1 融合蛋白相似性测量：在无阈值集线器检测中的应用

一项详尽的文献调查表明，发现蛋白质/基因相似性是解决广泛的生物信息学问题的重要一步，例如预测蛋白质-蛋白质相互作用、分析蛋白质-蛋白质相互作用网络 (PPIN)、基因优先排序和疾病基因/蛋白质检测。在本文中，我们提出了一种改进的三合一融合蛋白相似性测量称为复星-II。它是建立在结合从基因本体 (GO)、PPIN 和蛋白质序列等三种潜在基因组/蛋白质组资源中提取的生物学知识的加权平均值之上的。此外，我们已经展示了所提出的措施在检测给定 PPIN 的潜在中枢蛋白中的应用。为此，我们提出了一种基于多目标聚类的蛋白质中心检测框架FuSim-II 用作底层邻近度测量。PPIN 的H. 智人和选择 M. Musculus 生物用于实验目的。与大多数现有的枢纽检测方法不同，所提出的技术不需要遵循任何蛋白质度截止值或阈值来定义枢纽。已经对提议的和现有的八种蛋白质相似性测量以及八种单/多目标聚类方法之间的效率进行了全面评估。内部集群有效性指标，如 Silhouette 和 Davies Bouldin (DB) 用于完成分析研究。此外，通过丰富必要性研究，对提出的和现有的五种中心蛋白检测算法进行了比较性能分析。报告的结果表明改进的性能FuSim-II 在识别功能相关蛋白质以及相关中枢蛋白质方面优于现有蛋白质相似性测量。补充材料可在http://csse.szu.edu.cn/staff/cuilz/eng/index.html .