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
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