TY - GEN
T1 - Bacterial biological mechanisms for functional module detection in PPI networks
AU - Yang, Cuicui
AU - Ji, Junzhong
AU - Zhang, Aidong
N1 - Publisher Copyright: © 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85013229377
U2 - 10.1109/BIBM.2016.7822539
DO - 10.1109/BIBM.2016.7822539
M3 - Conference contribution
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 318
EP - 323
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Y2 - 15 December 2016 through 18 December 2016
ER -