Paper Info
Title
BFO-FMD: bacterial foraging optimization for functional module detection in protein-protein interaction networks.
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.
Year
DOI
Venue
2018
10.1007/s00500-017-2584-9
Soft Comput.
Keywords
Field
DocType
Computational biology, Protein–protein interaction network, Functional module detection, Bacterial foraging optimization
Protein protein interaction network,Computer science,Swarm intelligence,Directed graph,Mechanism (biology),Artificial intelligence,Functional module,Machine learning,Foraging
Journal
Volume
Issue
ISSN
22
10
1432-7643
Citations 
PageRank 
References 
0
0.34
25
Authors
3
Name
Order
Citations
PageRank
Cuicui Yang1286.47
Junzhong Ji222229.30
Aidong Zhang32970405.63