基于基因本体和核-附属的蛋白质复合物识别算法
Identifying Protein Complexes Based on Gene Ontology and Core-Attachment Structure
DOI: 10.12677/CSA.2018.88140, PDF,    科研立项经费支持
作者: 于 杨*：沈阳师范大学，辽宁 沈阳
关键词: 基因本体；核-附属结构；有权网络；Gene Ontology； Core-Attachment Structure； Weighted Network
摘要: 蛋白质复合物由一组具有特定生物功能的蛋白质组成。使用计算方法从生物网络中预测蛋白质复合物对于理解生物活动的机制和疾病的发病机理具有重要的现实意义。传统的复合物识别算法通常仅基于网络拓扑结构，忽略生物特征和噪声数据对复合物识别性能的影响。针对该问题，本文提出一种基因本体和核-附属结构的蛋白质复合物识别算法，首先通过语义相似性融合蛋白质相互作用网络和基因本体信息构建有权图模型；其次，设计以局部子图直径和密度为聚类条件的核-附属结构的复合物识别算法GCA。最后，GCA和三个经典的方法在两个复合物数据集中进行比较和分析。实验结果表明，GCA在召回率、f度量和功能富集分析方面的表现均显著优于CFinder，MCode和MCL。
Abstract: Protein complexes are composed of a group of proteins with specific biological functions. Computational methods for protein complexes prediction from biological networks have important practical implications for understanding the mechanisms of biological activity and the pathogenesis of diseases. Some of traditional algorithms are usually based only on network topologies, ignoring the impact of biological information and noise data on complex prediction. Aiming at this problem, we propose a protein complex identification algorithm based on gene ontology and core-attachment structure. Firstly, a weighted graph model is constructed based on semantic similarity by combining protein interaction network with gene ontology information. Secondly, a complex identification algorithm GCA is designed with local subgraph diameter and density as clustering conditions. Finally, GCA is compared with three methods in two real complex data sets. The experimental results indicate that GCA performances significantly better than CFinder, MCode and MCL in terms of recall, f-measure and functional enrichment analysis.
文章引用：于杨. 基于基因本体和核-附属的蛋白质复合物识别算法[J]. 计算机科学与应用, 2018, 8(8): 1300-1308. https://doi.org/10.12677/CSA.2018.88140

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