The Effect of Sequence Complexity on the Construction of Protein-Protein Interaction Networks

被引:0
|
作者
Kargar, Mehdi [1 ]
An, Aijun [1 ]
机构
[1] York Univ, Dept Comp Sci & Engn, N York, ON M3J 1P3, Canada
来源
BRAIN INFORMATICS, BI 2010 | 2010年 / 6334卷
关键词
COMMUNITY STRUCTURE; GENOMIC SEQUENCES; ENTROPY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, the role of sequence complexity in the construction of important nodes in protein-protein interaction (PPI) networks is investigated. We use two complexity measures, linguistic complexity and Shanon entropy, to measure the complexity of protein sequences. Three different datasets of yeast PPI networks are used to conclude the results. It has been shown that there are two important types of nodes in the PPI networks, which are hub and bottleneck nodes. It has been shown recently that hubs and bottlenecks tend to be essential in the process of evolution. Better understanding of the properties of these two types of nodes will shed light on why proteins interact with each other in the observed manner. We show that the sequence complexity of hubs are lower than that of non-hubs. But the difference is not significant in most cases. On the other hand, the sequence complexity of bottlenecks are lower than that of non-bottlenecks and the difference is significant in most cases. Modularity has an effective role in the construction of PPI networks. We find that there is no significant difference in the node complexity among different modules in a PPI network.
引用
收藏
页码:308 / 319
页数:12
相关论文
共 50 条
  • [1] Interdependent Patterns in Protein-Protein Interaction Networks
    Sun, Peng Gang
    Quan, Yining
    Miao, Qiguang
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2020, 7 (04): : 3257 - 3265
  • [2] A survey of computational methods in protein-protein interaction networks
    Rasti, Saeid
    Vogiatzis, Chrysafis
    ANNALS OF OPERATIONS RESEARCH, 2019, 276 (1-2) : 35 - 87
  • [3] Clustering and Summarizing Protein-Protein Interaction Networks: A Survey
    Bhowmick, Sourav S.
    Seah, Boon Siew
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2016, 28 (03) : 638 - 658
  • [4] HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks
    Wang, Xiaomin
    Wang, Zhengzhi
    Ye, Jun
    JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY, 2011,
  • [5] Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity
    Kim, Jongkwang
    Tan, Kai
    BMC BIOINFORMATICS, 2010, 11
  • [6] Prediction of Protein-Protein Interaction Sites Using Back Propagation Neural Networks
    Wang, Feilu
    Song, Yang
    2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC), 2013, : 1057 - 1061
  • [7] Fast algorithms for detecting overlapping functional modules in protein-protein interaction networks
    Sun, Peng Gang
    Gao, Lin
    CIBCB: 2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 2009, : 247 - 254
  • [8] A comprehensive review and evaluation of computational methods for identifying protein complexes from protein-protein interaction networks
    Wu, Zhourun
    Liao, Qing
    Liu, Bin
    BRIEFINGS IN BIOINFORMATICS, 2020, 21 (05) : 1531 - 1548
  • [9] Density Based Merging Search of Functional Modules in Protein-Protein Interaction (PPI) Networks
    Wang, Wei
    Ma, Jinwen
    ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, 2010, 6215 : 634 - +
  • [10] Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks
    Yang, Le
    Chen, Runpu
    Melendy, Thomas
    Goodison, Steve
    Sun, Yijun
    CANCERS, 2023, 15 (16)