Optimization Biclustering Algorithm Based on Parallel Non-Dominated
Sorting Genetic AlgorithmⅡ

Abstract

Abstract:

Biclustering is a very practical data mining technique in microarray gene expression data analysis and it is a way to cluster
both microarray genes and conditions simultaneously, which is used to excavate the biological mode reflected by the gene subset set
under the condition subset. The processing efficiency of traditional biclustering algorithm for large gene expression data is low, so this
paper explores a new research method and idea, i.e. applying gene expression data biclustering on jMetal platform. Also the parallel
strategy is proposed to improve the efficiency of the algorithm. Experiments on two datasets, yeast cell dataset and human B-cell
dataset, show that our approach exhibits better and more stable performance than other multi-objective biclustering algorithms.

Key words: gene expression data, biclustering jMetal, parallel algorithm, genetic algorithm

CLC Number:

TP301.6

WANG Limei，CAI Jianfeng，ZHONG Yiwen，PENG Fuqiang. Optimization Biclustering Algorithm Based on Parallel Non-Dominated
Sorting Genetic AlgorithmⅡ[J]. J4, 2014, 13(12): 15-20.

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