Clustering PPI data based on bacteria foraging optimization algorithm

Xiujuan Lei; Shuang Wu; Liang Ge; Aidong Zhang

doi:10.1109/BIBM.2011.18

Clustering PPI data based on bacteria foraging optimization algorithm

Xiujuan Lei
, Shuang Wu
, Liang Ge
, Aidong Zhang

Department of Computer Science and Engineering

University at Buffalo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This paper proposed a novel method using Bacteria Foraging Optimization(BFO) algorithm to avoid the influence of cluster number on experimental result of clustering PPI networks. The initial position that the bacterium located in was considered to be the cluster center and the positions that the bacterium moved were regarded as the adjacent nodes of cluster center. The algorithm classified the nodes selected in the chemo tactic operation into cluster when executing the reproduction and elimination-dispersal operations. The procedure kept on creating new clusters until all the nodes were grouped into the clusters. The simulation result showed that the algorithm not only effectively improved the accuracy of cluster result, but also automatically determined the cluster number.

Original language	English
Title of host publication	Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Pages	96-99
Number of pages	4
DOIs	https://doi.org/10.1109/BIBM.2011.18
State	Published - 2011
Event	2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 - Atlanta, GA, United States Duration: Nov 12 2011 → Nov 15 2011

Publication series

Name	Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

Conference

Conference	2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Country/Territory	United States
City	Atlanta, GA
Period	11/12/11 → 11/15/11

Keywords

PPI networks
accumulation coefficient of edge
bacteria foraging optimization algorithm

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10.1109/BIBM.2011.18

Cite this

Lei, X., Wu, S., Ge, L., & Zhang, A. (2011). Clustering PPI data based on bacteria foraging optimization algorithm. In Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 (pp. 96-99). Article 6120414 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011). https://doi.org/10.1109/BIBM.2011.18

@inproceedings{8a1627028d214173888575596a196288,

title = "Clustering PPI data based on bacteria foraging optimization algorithm",

abstract = "This paper proposed a novel method using Bacteria Foraging Optimization(BFO) algorithm to avoid the influence of cluster number on experimental result of clustering PPI networks. The initial position that the bacterium located in was considered to be the cluster center and the positions that the bacterium moved were regarded as the adjacent nodes of cluster center. The algorithm classified the nodes selected in the chemo tactic operation into cluster when executing the reproduction and elimination-dispersal operations. The procedure kept on creating new clusters until all the nodes were grouped into the clusters. The simulation result showed that the algorithm not only effectively improved the accuracy of cluster result, but also automatically determined the cluster number.",

keywords = "PPI networks, accumulation coefficient of edge, bacteria foraging optimization algorithm",

author = "Xiujuan Lei and Shuang Wu and Liang Ge and Aidong Zhang",

year = "2011",

doi = "10.1109/BIBM.2011.18",

language = "English",

isbn = "9780769545745",

series = "Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011",

pages = "96--99",

booktitle = "Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011",

note = "2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 ; Conference date: 12-11-2011 Through 15-11-2011",

}

Lei, X, Wu, S, Ge, L & Zhang, A 2011, Clustering PPI data based on bacteria foraging optimization algorithm. in Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011., 6120414, Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, pp. 96-99, 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, Atlanta, GA, United States, 11/12/11. https://doi.org/10.1109/BIBM.2011.18

Clustering PPI data based on bacteria foraging optimization algorithm. / Lei, Xiujuan; Wu, Shuang; Ge, Liang et al.
Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011. 2011. p. 96-99 6120414 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Clustering PPI data based on bacteria foraging optimization algorithm

AU - Lei, Xiujuan

AU - Wu, Shuang

AU - Ge, Liang

AU - Zhang, Aidong

PY - 2011

Y1 - 2011

N2 - This paper proposed a novel method using Bacteria Foraging Optimization(BFO) algorithm to avoid the influence of cluster number on experimental result of clustering PPI networks. The initial position that the bacterium located in was considered to be the cluster center and the positions that the bacterium moved were regarded as the adjacent nodes of cluster center. The algorithm classified the nodes selected in the chemo tactic operation into cluster when executing the reproduction and elimination-dispersal operations. The procedure kept on creating new clusters until all the nodes were grouped into the clusters. The simulation result showed that the algorithm not only effectively improved the accuracy of cluster result, but also automatically determined the cluster number.

AB - This paper proposed a novel method using Bacteria Foraging Optimization(BFO) algorithm to avoid the influence of cluster number on experimental result of clustering PPI networks. The initial position that the bacterium located in was considered to be the cluster center and the positions that the bacterium moved were regarded as the adjacent nodes of cluster center. The algorithm classified the nodes selected in the chemo tactic operation into cluster when executing the reproduction and elimination-dispersal operations. The procedure kept on creating new clusters until all the nodes were grouped into the clusters. The simulation result showed that the algorithm not only effectively improved the accuracy of cluster result, but also automatically determined the cluster number.

KW - PPI networks

KW - accumulation coefficient of edge

KW - bacteria foraging optimization algorithm

UR - https://www.scopus.com/pages/publications/84862938233

U2 - 10.1109/BIBM.2011.18

DO - 10.1109/BIBM.2011.18

M3 - Conference contribution

SN - 9780769545745

T3 - Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

SP - 96

EP - 99

BT - Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

T2 - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

Y2 - 12 November 2011 through 15 November 2011

ER -

Clustering PPI data based on bacteria foraging optimization algorithm

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Keywords

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