Degree distribution of protein-protein interaction networks formed by gene duplication

被引：0
|
作者：
Ma, Z. [1 ]
Xu, H. [1 ]
Wu, X. G. [2 ]
机构：
[1] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON, Canada
[2] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
来源：
BIOINFORMATICS AND BIOMEDICAL ENGINEERING: NEW ADVANCES | 2016年
关键词：
protein-protein interaction network; gene duplication; degree distribution function; random duplication graph; scale-free distribution; DROSOPHILA-MELANOGASTER;
D O I：
暂无
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
This paper proposes a degree distribution function for protein-protein interaction networks. It is derived from modeling the protein-protein interaction network as a random duplication graph with sparse initial state, where the duplication particularly refers to the gene duplication that produces new proteins and grows the network. This degree distribution reveals some characteristics of protein-protein interaction networks: the majority of nodes are sparsely connected while highly connected proteins also exist; as the growth process continues, more and more highly connected proteins will be produced. Finally, we also show that compared to the widely used scale-free distribution, our degree distribution can fit the experimental data better.
引用
收藏
页码：91 / 97
页数：7
相关论文
共 50 条
  • [11] Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks
    Cao, Renzhi
    Cheng, Jianlin
    METHODS, 2016, 93 : 84 - 91
  • [12] Prediction and characterization of protein-protein interaction networks in swine
    Wang, Fen
    Liu, Min
    Song, Baoxing
    Li, Dengyun
    Pei, Huimin
    Guo, Yang
    Huang, Jingfei
    Zhang, Deli
    PROTEOME SCIENCE, 2012, 10
  • [13] Protein-protein interaction networks as miners of biological discovery
    Wang, Steven
    Wu, Runxin
    Lu, Jiaqi
    Jiang, Yijia
    Huang, Tao
    Cai, Yu-Dong
    PROTEOMICS, 2022, 22 (15-16)
  • [14] Construction and contextualization approaches for protein-protein interaction networks
    Badkas, Apurva
    De Landtsheer, Sebastien
    Sauter, Thomas
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2022, 20 : 3280 - 3290
  • [15] Active learning for protein function prediction in protein-protein interaction networks
    Xiong, Wei
    Xie, Luyu
    Zhou, Shuigeng
    Guan, Jihong
    NEUROCOMPUTING, 2014, 145 : 44 - 52
  • [16] Collective prediction of protein functions from protein-protein interaction networks
    Wu, Qingyao
    Ye, Yunming
    Ng, Michael K.
    Ho, Shen-Shyang
    Shi, Ruichao
    BMC BIOINFORMATICS, 2014, 15
  • [17] Collective prediction of protein functions from protein-protein interaction networks
    Qingyao Wu
    Yunming Ye
    Michael K Ng
    Shen-Shyang Ho
    Ruichao Shi
    BMC Bioinformatics, 15
  • [18] Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology
    Peterson, G. Jack
    Presse, Steve
    Peterson, Kristin S.
    Dill, Ken A.
    PLOS ONE, 2012, 7 (06):
  • [19] Multimodal Deep Representation Learning for Protein-Protein Interaction Networks
    Zhang, Da
    Kabuka, Mansur R.
    PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2018, : 595 - 602
  • [20] Protein-protein interaction network and significant gene analysis of osteoporosis
    Wu, X. M.
    Ma, X.
    Tang, C.
    Xie, K. N.
    Liu, J.
    Guo, W.
    Yan, Y. L.
    Shen, G. H.
    Luo, E. P.
    GENETICS AND MOLECULAR RESEARCH, 2013, 12 (04): : 4751 - 4759
12345