Soft Computing

, Volume 22, Issue 10, pp 3395–3416 | Cite as

BFO-FMD: bacterial foraging optimization for functional module detection in protein–protein interaction networks

Methodologies and Application
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Abstract

Identifying functional modules in PPI networks contributes greatly to the understanding of cellular functions and mechanisms. Recently, the swarm intelligence-based approaches have become effective ways for detecting functional modules in PPI networks. This paper presents a new computational approach based on bacterial foraging optimization for functional module detection in PPI networks (called BFO-FMD). In BFO-FMD, each bacterium represents a candidate module partition encoded as a directed graph, which is first initialized by a random-walk behavior according to the topological and functional information between protein nodes. Then, BFO-FMD utilizes four principal biological mechanisms, chemotaxis, conjugation, reproduction, and elimination and dispersal to search for better protein module partitions. To verify the performance of BFO-FMD, we compared it with several other typical methods on three common yeast datasets. The experimental results demonstrate the excellent performances of BFO-FMD in terms of various evaluation metrics. BFO-FMD achieves outstanding Recall, F-measure, and PPV while performing very well in terms of other metrics. Thus, it can accurately predict protein modules and help biologists to find some novel biological insights.

Keywords

Computational biology Protein–protein interaction network Functional module detection Bacterial foraging optimization 

Notes

Acknowledgements

This work was partly supported by the NSFC Research Program (61672065, 61375059) and the Beijing Municipal Education Research Plan Key Project (Beijing Municipal Fund Class B) (KZ201410005004).

Compliance with ethical standards

Conflict of interest

All the authors declare that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

500_2017_2584_MOESM1_ESM.rar (4.5 mb)
Supplementary material 1 (rar 4630 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloUSA

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