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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Genomics

Open Access Proceedings

Multi-objective dynamic population shuffled frog-leaping biclustering of microarray data

Junwan Liu1, Zhoujun Li23, Xiaohua Hu45*, Yiming Chen6 and Feifei Liu7

Author affiliations

1 School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China

2 State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China

3 Beijing Key Laboratory of Network Technology, Beihang University, Beijing 100191, China

4 Department of Computer Science, Central China Normal University, Wuhan 430079, China

5 College of Information Science, Drexel University, Philadelphia, PA 19104, USA

6 School of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China

7 Library, Central South University of Forestry and Technology, Changsha 410004, China

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Citation and License

BMC Genomics 2012, 13(Suppl 3):S6  doi:10.1186/1471-2164-13-S3-S6

Published: 11 June 2012



Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets.


This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions.


The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.