Microarray biclustering with crowding based MOACO

Junwan Liu; Zhoujun Li; Xiaohua Hu; Yiming Chen

doi:10.1109/BIBM.2009.23

Microarray biclustering with crowding based MOACO

Junwan Liu^*
, Zhoujun Li
, Xiaohua Hu
, Yiming Chen

^*Corresponding author for this work

School of Computer Science and Engineering

Central South University of Forestry & Technology
National University of Deference Technology
Drexel University
Hunan Agricultural University

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

1 Scopus citations

Abstract

Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. Ant Colony Optimization (ACO) algorithms have been shown to be effective problem solving strategies for Multiple Objective Optimization (MOO). Multiple Objective Ant colony optimization (MOACO) mainly focuses on solving the multiple objective combinatorial optimization problems. This paper incorporates crowding update technology into MOACOB and proposes a novel crowding based MOACO biclustering algorithm to mine biclusters from microarray dataset. Experimental results are shown for biclustering algorithm on two real gene expression dataset.

Original language	English
Title of host publication	2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
Pages	170-173
Number of pages	4
DOIs	https://doi.org/10.1109/BIBM.2009.23
State	Published - 2009
Event	2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009 - Washington, D.C., United States Duration: 1 Nov 2009 → 4 Nov 2009

Publication series

Name	2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009

Conference

Conference	2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
Country/Territory	United States
City	Washington, D.C.
Period	1/11/09 → 4/11/09

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10.1109/BIBM.2009.23

Cite this

@inproceedings{db8748d1335c4254a7d1b436962118a7,

title = "Microarray biclustering with crowding based MOACO",

abstract = "Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. Ant Colony Optimization (ACO) algorithms have been shown to be effective problem solving strategies for Multiple Objective Optimization (MOO). Multiple Objective Ant colony optimization (MOACO) mainly focuses on solving the multiple objective combinatorial optimization problems. This paper incorporates crowding update technology into MOACOB and proposes a novel crowding based MOACO biclustering algorithm to mine biclusters from microarray dataset. Experimental results are shown for biclustering algorithm on two real gene expression dataset.",

author = "Junwan Liu and Zhoujun Li and Xiaohua Hu and Yiming Chen",

year = "2009",

doi = "10.1109/BIBM.2009.23",

language = "英语",

isbn = "9780769538853",

series = "2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009",

pages = "170--173",

booktitle = "2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009",

note = "2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009 ; Conference date: 01-11-2009 Through 04-11-2009",

}

Liu, J, Li, Z, Hu, X & Chen, Y 2009, Microarray biclustering with crowding based MOACO. in 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009., 5341822, 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, pp. 170-173, 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, Washington, D.C., United States, 1/11/09. https://doi.org/10.1109/BIBM.2009.23

Microarray biclustering with crowding based MOACO. / Liu, Junwan; Li, Zhoujun; Hu, Xiaohua et al.
2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009. 2009. p. 170-173 5341822 (2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009).

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

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AU - Liu, Junwan

AU - Li, Zhoujun

AU - Hu, Xiaohua

AU - Chen, Yiming

PY - 2009

Y1 - 2009

N2 - Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. Ant Colony Optimization (ACO) algorithms have been shown to be effective problem solving strategies for Multiple Objective Optimization (MOO). Multiple Objective Ant colony optimization (MOACO) mainly focuses on solving the multiple objective combinatorial optimization problems. This paper incorporates crowding update technology into MOACOB and proposes a novel crowding based MOACO biclustering algorithm to mine biclusters from microarray dataset. Experimental results are shown for biclustering algorithm on two real gene expression dataset.

AB - Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. Ant Colony Optimization (ACO) algorithms have been shown to be effective problem solving strategies for Multiple Objective Optimization (MOO). Multiple Objective Ant colony optimization (MOACO) mainly focuses on solving the multiple objective combinatorial optimization problems. This paper incorporates crowding update technology into MOACOB and proposes a novel crowding based MOACO biclustering algorithm to mine biclusters from microarray dataset. Experimental results are shown for biclustering algorithm on two real gene expression dataset.

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DO - 10.1109/BIBM.2009.23

M3 - 会议稿件

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T3 - 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009

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BT - 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009

T2 - 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009

Y2 - 1 November 2009 through 4 November 2009

ER -

Microarray biclustering with crowding based MOACO

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