《计算机应用研究》|Application Research of Computers

基于加权网络和局部适应度的蛋白质复合物识别算法

Algorithm for identifying protein complexes based on weighted network and local fitness

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作者 刘翠翠,孙伟
机构 1.长沙医学院 信息工程学院,长沙 410219;2.信息工程大学 网络空间安全学院,郑州 450000
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文章编号 1001-3695(2018)08-2308-03
DOI 10.3969/j.issn.1001-3695.2018.08.017
摘要 通常在蛋白质网络中挖掘稠密子图或模块来识别其中的蛋白质复合物,限制了其应用范围和识别的准确性。针对该问题,提出了一种基于加权网络和局部适应度的蛋白质复合物识别算法。该算法综合稠密子图的密度指标和模块性定义了新的局部适应度函数,并基于边聚集系数构建加权的蛋白质网络,根据权值选择边,在加权蛋白质网络中将种子边不断聚类扩展,挖掘综合适应度最大的子图,从而识别出蛋白质复合物。在多个真实蛋白质网络中的实验表明,该算法能够有效提升蛋白质复合物识别的准确性。
关键词 加权网络;适应度;蛋白质复合物识别;模块性
基金项目 国家自然科学基金资助项目(F010103)
本文URL http://www.arocmag.com/article/01-2018-08-017.html
英文标题 Algorithm for identifying protein complexes based on weighted network and local fitness
作者英文名 Liu Cuicui, Sun Wei
机构英文名 1.CollegeofInformationEngineering,ChangshaMedicalUniversity,Changsha410219,China;2.InstituteforNetworkSecurity,InformationEngineeringUniversity,Zhengzhou450000,China
英文摘要 Usually, the ways about mining dense subgraph or module would limit their scope of application and recognition accuracy on protein complexes identification. To solve this problem, this paper proposed a novel protein complex recognition algorithm based on weighted network and local fitness. By integrating density of subgraph and modularity, it defined a new local fitness function, and used edge clustering coefficient to construct the weighted protein network, selected some seed edges according to their weights, then extended the clustering around the seed edge until gaining a biggest protein subgraph with maximum comprehensive fitness. Experiments in yeast protein networks show that, this algorithm can effectively improve the accuracy on protein complexes identification.
英文关键词 weighted networks; fitness; protein complexes identification; modularity
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收稿日期 2017/4/12
修回日期 2017/5/16
页码 2308-2310,2327
中图分类号 TP391
文献标志码 A