Mining Protein Complexes Based on Topology Potential Weight in Dynamic Protein-Protein Interaction Networks

LEI Xiu-juan; GAO Yin; GUO Ling

doi:10.3969/j.issn.0372-2112.2018.01.020

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Mining Protein Complexes Based on Topology Potential Weight in Dynamic Protein-Protein Interaction Networks

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- Mining Protein Complexes Based on Topology Potential Weight in Dynamic Protein-Protein Interaction Networks
- Acta Electronica Sinica Vol. 46, Issue 1, Pages: 145-151(2018)
- 作者机构：
  
  1. 陕西师范大学计算机科学学院,陕西,西安,710119
  2. 陕西师范大学生命科学学院,陕西,西安,710119
  3. 陕西师范大学计算机科学学院,陕西,西安,710119
  4. 陕西师范大学生命科学学院,陕西,西安,710119
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61672334, No.61502290, No.61401263);Industrial Science and Technology Research and Development Project of Shaanxi Province (No.2015GY016);China Postdoctoral Science Foundation (No.2015M582606)
- DOI：10.3969/j.issn.0372-2112.2018.01.020
  CLC： TP301.6
- Published Online：25 January 2018，
  
  Published：2018
- 稿件说明：
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LEI Xiu-juan, GAO Yin, GUO Ling. Mining Protein Complexes Based on Topology Potential Weight in Dynamic Protein-Protein Interaction Networks[J]. Acta Electronica Sinica, 2018, 46(1): 145-151.
DOI：

LEI Xiu-juan, GAO Yin, GUO Ling. Mining Protein Complexes Based on Topology Potential Weight in Dynamic Protein-Protein Interaction Networks[J]. Acta Electronica Sinica, 2018, 46(1): 145-151. DOI： 10.3969/j.issn.0372-2112.2018.01.020.

摘要

从动态蛋白质相互作用（PPI）网络中挖掘蛋白质复合物是当前复合物挖掘研究的一个热点，但是目前大都采用未加权网络进行聚类分析，由于不能准确地描述网络的拓扑特性，因此其正确率不高.鉴于此，本文提出采用拓扑势场的方法来构造加权网络，网络中的每一个蛋白质都被视作一个物理粒子，在它周围存在一个虚拟的作用场，由此网络中所有蛋白质的相互作用联合形成一个拓扑势场，文中定义了结点间的拓扑势的概念，并以此来构造加权网络，之后采用马尔科夫聚类算法在DIP数据和Krogan数据上进行复合物挖掘.与其它经典算法相比，该方法的

precision

和

-measure值较高，能更好地识别蛋白质复合物.

Abstract

At present

many researchers focus on identifying protein complexes in the dynamic protein-protein interaction (PPI) network. But most of the methods have been used in unweighted network

the accuracy is not high since they could not accurately describe the network topology characteristics. In this paper

we put forward the topology potential to construct the weighted network. A protein can be regarded as a physical particle in the network which has a virtual field around it

and the interaction of all proteins forms a topology potential field. By computing the value of topology potential between nodes

the weighted network is constructed

and Markov clustering algor

ithm is used to identify protein complexes. The experimental results compared with the classic algorithms on DIP data and Krogan data indicate that their precision and

-measure value are higher and the proposed algorithm is more suitable to identify the protein complexes.

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