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Multiobjective Optizition Shuffled Frog-leaping Biclustering
Conference proceeding

Multiobjective Optizition Shuffled Frog-leaping Biclustering

Junwan Liu, Zhoujun Li, Xiaohua Hu, Junwan Liu and Yiming Chen
2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS
01 Jan 2011

Abstract

Computer Science, Interdisciplinary Applications Computer Science, Theory & Methods Life Sciences & Biomedicine Mathematical & Computational Biology Science & Technology Computer Science Technology
Biclustering of DNA microarray data that can mine significant patterns to help in understanding gene regulation and interactions. This is a classical multi-objective optimization problem (MOP). Recently, many researchers have developed stochastic search methods that mimic the efficient behavior of species such as ants, bees, birds and frogs, as a means to seek faster and more robust solutions to complex optimization problems. The particle swarm optimization(PSO) is a heuristics-based optimization approach simulating the movements of a bird flock finding food. The shuffled frog leaping algorithm (SFLA) 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. This paper introduces SFL algorithm to solve biclustering of microarray data, and proposes a novel multi-objective shuffled frog leaping biclustering(MOSFLB) algorithm to mine coherent patterns from microarray data. Experimental results on two real datasets show that our approach can effectively find significant biclusters of high quality.

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Web of Science research areas
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Mathematical & Computational Biology
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