JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (1): 21-30.doi: 10.6040/j.issn.1672-3961.0.2017.291

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Flower pollination algorithm-based functional module detection in protein-protein interaction networks

WU Hongyan, JI Junzhong*   

  1. Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
  • Received:2017-06-09 Online:2018-02-20 Published:2017-06-09

Abstract: Revealing unknown functions of proteins were one of the core contents of proteomics in the post gene era, where it had become a hotspot to use the swarm intelligence-based approaches to identify functional modules in protein-protein interaction networks(PPIN). An approach based on flower pollination algorithm to detect functional modules in PPIN was proposed. Each pollen in the population was encoded by a random walk and the population was optimized by using two mechanisms of self-pollination and cross-pollination which were specially owned by flower pollination algorithm. More specially, the strategies of recombination and better-solution selection were adopted in the self-pollination while the mutation strategies based on Levy mechanism and an adaptive individual-difference were employed in the cross-pollination. The four strategies together promoted the evolution of the population from different angles. The simulation experiments on three public data sets showed that the proposed algorithm had not only excellent overall performance but also absolute superiority in terms of two comprehensive indicators F-measure and accuracy compared with the other six classical algorithms.

Key words: self-pollination, functional module detection, flower pollination algorithm, cross-pollination, protein-protein interaction network

CLC Number: 

  • Q811.4
[1] VAKOADJEI D, FU W, WALLIN C, et al. HIV-1,human interaction database: current status and new features[J]. Nucleic Acids Research, 2014, 43(D1):566-570.
[2] BHOWMICK S S, SEAH B S. Clustering and summarizing protein-protein interaction networks: a survey[J]. IEEE Transactions on Knowledge & Data Engineering, 2016, 28(3):638-658.
[3] JI J, ZHANG A, LIU C, et al. Survey: functional module detection from protein-protein interaction networks[J]. IEEE Transactions on Knowledge & Data Engineering, 2014, 26(2):261-277.
[4] 李敏, 孟祥茂. 动态蛋白质网络的构建、分析及应用研究进展[J].计算机研究与发展, 2017,54(6):1281-1299. LI Min, MENG Xiangmao. The construction, analysis, and applications of dynamic protein-protein interaction networks[J]. Journal of Computer Research and Development, 2017, 54(6):1281-1299.
[5] 冀俊忠, 刘志军, 刘红欣,等. 蛋白质相互作用网络功能模块检测的研究综述[J]. 自动化学报, 2014, 40(4):577-593. JI Junzhong, LIU Zhijun, LIU Hongxin, et al. An overview of research on functional module detection for protein-protein interaction networks[J]. Acta Automatica Sinica, 2014, 40(4):577-593.
[6] BADER G D, HOGUE C W. An automated method for finding molecular complexes in large protein interaction networks[J]. BMC Bioinformatics, 2003, 4(1):2-28.
[7] ALDECOA R, MARIN I. Jerarca: efficient analysis of complex networks using hierarchical clustering[J]. PLOS ONE, 2010, 5(7):e11585.
[8] WU M, LI X, KWOH C K, et al. A core-attachment based method to detect protein complexes in PPI networks[J]. BMC Bioinformatics, 2009, 10(1):169-178.
[9] ADAMCSEK B, PALLA G, FARKAS I J, et al. CFinder: locating cliques and overlapping modules in biological networks[J]. Bioinformatics, 2006, 22(8):1021-1023.
[10] DONGEN S. A cluster algorithm for graphs. technical report INS-R0010[R]. Amsterdam: National Research Institute for Mathematics and Computer Science in the Netherlands, 2000.
[11] 雷秀娟, 黄旭, 吴爽, 等. 基于连接强度的PPI网络蚁群优化聚类算法[J]. 电子学报, 2012, 40(4):695-702. LEI Xiujuan, HUANG Xu, WU Shuang, et al. Joint strength based ant colony optimization clustering algorithm for PPI networks[J].Chinese Journal of Electronics, 2012, 40(4):695-702.
[12] JI J Z, JIAO L, YANG C C, et al. MAE-FMD: multi-agent evolutionary method for functional module detection in protein-protein interaction networks[J]. BMC Bioinformatics, 2014, 15(1):325-350.
[13] JI J, LIU Z, ZHANG A, et al. Improved ant colony optimization for detecting functional modules in protein-protein interaction networks[C] // International Conference on Information Computing and Applications. Berlin, Germany: Springer, 2012:404-413.
[14] YANG C, JI J, ZHANG A. Bacterial biological mechanisms for functional module detection in PPI networks[C] // IEEE International Conference on Bioinformatics and Biomedicine. Shenzhen, China: IEEE Computer Society, 2016:318-323.
[15] YANG X S. Flower pollination algorithm for global optimization[J]. Lecture Notes in Computer Science, 2012, 7445:240-249.
[16] CHIROMA H, KHAN A, ABUBAKAR A I, et al. A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm[J]. Applied Soft Computing, 2016, 48:50-58.
[17] SHILAJA C, RAVI K. Optimization of emission/economic dispatch using euclidean affine flower pollination algorithm(eFPA)and binary FPA(BFPA)in solar photo voltaic generation[J]. Renewable Energy, 2017, 107:550-566.
[18] 廖宏泽. 拟南芥蛋白激酶PTI1-5在花粉管和根毛生长中的作用研究[D].北京:中国农业大学,2017. LIAO Hongze. Study of the roles of arabidopsis protein kinase PTI1-5 in growth of pollentubes and root hairs[D]. Beijing:China Agricultural University, 2017.
[19] 达尔文. 植物界异花受精和自花受精的效果[M]. 北京: 科学出版社, 1959. DARWIN. The effect of both cross-fertilization and self-fertilization in plantae[M]. Beijing: Science Press, 1959.
[20] LING Ying, ZHOU Yongquan, LUO Qifang. Lévy flight trajectory-based whale optimization algorithm for global optimization[J]. IEEE Access, 2017, 5:6168-6186.
[21] FRIEDEL C C, KRUMSIEK J, ZIMMER R. Bootstrapping the interactome:unsupervise didentification of protein complexes in Yeast[J]. Journal of Computational Biology, 2009, 16(8):971-987.
[22] LI X, WU M, KWOH C K, et al. Computational approaches for detecting protein complexes from protein interaction networks: a survey[J]. Bmc Genomics, 2010, 11(S1):S3.
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