A comparative study on network motif discovery algorithms.

IF 0.2 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yusuf Kavurucu
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引用次数: 8

Abstract

Subgraphs that occur in complex networks with significantly higher frequency than those in randomised networks are called network motifs. Such subgraphs often play important roles in the functioning of those networks. Finding network motifs is a computationally challenging problem. The main difficulties arise from the fact that real networks are large and the size of the search space grows exponentially with increasing network and motif size. Numerous methods have been developed to overcome these challenges. This paper provides a comparative study of the key network motif discovery algorithms in the literature and presents their algorithmic details on an example network.

网络motif发现算法的比较研究。
在复杂网络中出现频率明显高于随机网络的子图称为网络基序。这些子图通常在这些网络的功能中起着重要的作用。寻找网络基元是一个具有计算挑战性的问题。主要的困难来自于这样一个事实:真实的网络很大,搜索空间的大小随着网络和母题大小的增加呈指数增长。已经开发了许多方法来克服这些挑战。本文对文献中主要的网络基序发现算法进行了比较研究,并在一个实例网络上给出了它们的算法细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.00
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.
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