用于蛋白质复合物预测的分层隐社区检测
Hierarchical hidden community detection for protein complex prediction
摘要：
动机：通过优化方法发现蛋白质相互作用（PPI）网络中的功能模块一直是生物学中的一个挑战。传统的算法简单地考虑了强蛋白质复合物，通过优化一些度量可以在原始网络中找到这些复合物，这对发现被强复合物屏蔽的弱和隐藏的复合物造成了障碍。此外，蛋白质复合物不仅密度不同，而且规模范围很大，使得其极难被检测。为此，我们提出了一个层次化的隐藏社区方法来预测蛋白质复合物。结果如下：我们提出了一种称为HirHide（分层隐藏社区检测）的方法，它可以与传统的社区检测方法相结合，使他们能够发现分层隐藏社区。这是第一个社区检测算法，可以找到一个层次结构，以及隐藏的结构。我们比较了三个传统的方法与他们的HirHide版本的性能。实验结果表明，以传统方法为基础的HirHide方法取得了较好的性能，有时甚至显著优于基线。
英文摘要：
Motivation: Discovering functional modules in protein-protein interaction (PPI) networks by optimization methods remains a longstanding challenge in biology. Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metrics, which causes obstacles for the discovery of weak and hidden complexes shielded by stronger complexes. Also, protein complexes are not only in different density but also in a large range of scale, making it extremely difficult to be detected. Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes. Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities. It is the first community detection algorithm that can find a hierarchical structure as well as hidden structure. We compare the performance of three traditional methods with their HirHide versions. Experimental results show that the HirHide methods using traditional methods as the base algorithms achieve better performance, sometimes even significantly outperform the baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
