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首页> 外文会议>International conference on advances in swarm intelligence >ACO Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks
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ACO Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks

机译：基于ACO的核心附件方法检测动态PPI网络中的蛋白质复合物

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摘要

Proteins complexes accomplish biological functions such as transcription of DNA and translation of mRNA. Detecting protein complexes correctly and efficiently is becoming a challenging task. This paper presents a novel algorithm, core-attachment based on ant colony optimization (CA-ACO), which detects complexes in three stages. Firstly, initialize the similarity matrix. Secondly, complexes are predicted by clustering in the dynamic PPI networks. In the step, the clustering coefficient of every node is also computed. A node whose clustering coefficient is greater than the threshold is added to the core protein set. Then we mark every neighbor node of core proteins with unique core label during picking and dropping. Thirdly, filtering processes are carried out to obtain the final complex set. Experimental results show that CA-ACO algorithm had great superiority in precision, recall and f-measure compared with the state-of-the-art methods such as ClusterONE, DPClus, MCODE and so on.
机译：蛋白质复合物可完成生物学功能，例如DNA转录和mRNA翻译。正确有效地检测蛋白质复合物正成为一项艰巨的任务。本文提出了一种新颖的算法，即基于蚁群优化的核心附着（CA-ACO），它可以分三个阶段检测复合物。首先，初始化相似度矩阵。其次，通过在动态PPI网络中进行聚类来预测复合物。在该步骤中，还计算每个节点的聚类系数。将聚类系数大于阈值的节点添加到核心蛋白集。然后，我们在拾取和放置过程中用唯一的核心标记标记核心蛋白的每个相邻节点。第三，进行滤波处理以获得最终的复数集合。实验结果表明，与目前最先进的方法（如ClusterONE，DPClus，MCODE等）相比，CA-ACO算法在精度，召回率和f-measure方面具有极大的优势。

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