Name
Abstract | ||
|---|---|---|
Identifying functional modules in protein-protein interaction (PPI) networks is fundamental to understand cellular organization, processes, and functions. As an emerging evolutionary computational technology, swarm intelligence approaches are now becoming a new research hotspot in identifying functional modules. This paper proposes a new computational approach based on bacterial biological mechanisms for functional module detection in PPI networks (called as BBM-FMD). In BBM-FMD, each bacterium is first initialized to a candidate module partition by a random walk behavior. Then four biological mechanisms of bacteria including chemotaxis, conjugation, reproduction, and elimination and dispersal are simulated to iteratively search for better protein module partitions. At last, two post-processing steps are carried out to refine the obtained module partition. The experimental results on two PPI datasets demonstrate the superior performance of BBM-FMD in detecting functional modules compared with several other algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BIBM.2016.7822539 | 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
bacterial biological mechanisms,functional module detection,PPI networks,protein-protein interaction networks,cellular organization,evolutionary computational technology,swarm intelligence approaches,BBM-FMD,random walk behavior,chemotaxis,cell conjugation,cell reproduction,cell elimination,cell dispersal,iterative search,protein module partitions,post-processing steps,PPI datasets | Computer science,Swarm intelligence,Mechanism (biology),Artificial intelligence,Bioinformatics,Functional module,Machine learning | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-5090-1612-9 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cuicui Yang | 1 | 28 | 6.47 |
| Junzhong Ji | 2 | 222 | 29.30 |
| Aidong Zhang | 3 | 2970 | 405.63 |