CeFunMO: A centrality based method for discovering functional motifs with application in biological networks

被引：3
|
作者：
Kouhsar, Morteza [1 ]
Razaghi-Moghadam, Zahra [2 ]
Mousavian, Zaynab [1 ]
Masoudi-Nejad, Ali [1 ]
机构：
[1] Univ Tehran, Inst Biochem & Biophys, Lab Syst Biol & Bioinformat LBB, Tehran, Iran
[2] Univ Tehran, FNST, Tehran, Iran
来源：
COMPUTERS IN BIOLOGY AND MEDICINE | 2016年 / 76卷
关键词：
Biological network; Centrality; Functional motif; List-colored graph; Protein complex; TOOL;
D O I：
10.1016/j.compbiomed.2016.07.009
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Detecting functional motifs in biological networks is one of the challenging problems in systems biology. Given a multiset of colors as query and a list-colored graph (an undirected graph with a set of colors assigned to each of its vertices), the problem is reduced to finding connected subgraphs, which best cover the multiset of query. To solve this NP-complete problem, we propose a new color-based centrality measure for list-colored graphs. Based on this newly-defined measure of centrality, a novel polynomial time algorithm is developed to discover functional motifs in list-colored graphs, using a greedy strategy. This algorithm, called CeFunMO, has superior running time and acceptable accuracy in comparison with other well-known algorithms, such as RANGI and GraMoFoNe. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：154 / 159
页数：6
相关论文
共 50 条
  • [1] Discovering novel cis-regulatory motifs using functional networks
    Ettwiller, LM
    Rung, J
    Birney, E
    [J]. GENOME RESEARCH, 2003, 13 (05) : 883 - 895
  • [2] Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks
    Koschutzki, Dirk
    Schreiber, Falk
    [J]. GENE REGULATION AND SYSTEMS BIOLOGY, 2008, 2 : 193 - 201
  • [3] Application of Fractal Theory on Motifs Counting in Biological Networks
    Joveini, Mahdi Barat Zadeh
    Sadri, Javad
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2018, 15 (02) : 613 - 623
  • [4] FUNCTIONAL EXPRESSION OF CENTRALITY IN A GRAPH - APPLICATION TO ASSESSMENT OF COMMUNICATION NETWORKS
    KAJITANI, Y
    MARUYAMA, T
    [J]. ELECTRONICS & COMMUNICATIONS IN JAPAN, 1976, 59 (07): : 9 - 17
  • [5] Dynamical systems for discovering protein complexes and functional modules from biological networks
    Li, Wenyuan
    Liu, Ying
    Huang, Hung-Chung
    Peng, Yanxiong
    Lin, Yongjing
    Ng, Wee-Keong
    Ong, Kok-Leong
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2007, 4 (02) : 233 - 250
  • [6] Generalized walks-based centrality measures for complex biological networks
    Estrada, Ernesto
    [J]. JOURNAL OF THEORETICAL BIOLOGY, 2010, 263 (04) : 556 - 565
  • [7] The MSS of Complex Networks with Centrality Based Preference and Its Application to Biomolecular Networks
    Wu, Lin
    Tang, Lingkai
    Li, Min
    Wang, Jianxin
    Wu, Fang-Xiang
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2016, : 229 - 234
  • [8] Application of dynamic expansion tree for finding large network motifs in biological networks
    Patra, Sabyasachi
    Mohapatra, Anjali
    [J]. PEERJ, 2019, 7
  • [9] A novel centrality method for weighted networks based on the Kirchhoff polynomial
    Qi, Xingqin
    Fuller, Edgar
    Luo, Rong
    Zhang, Cun-quan
    [J]. PATTERN RECOGNITION LETTERS, 2015, 58 : 51 - 60
  • [10] Discovering Frequent Probability Pattern in Uncertain Biological Networks by Circuit Simulation Method
    Wang, Chunyan
    Qiu, Kunpu
    Zhong, Wei
    He, Jieyue
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2013,
12345