A NEURAL-NETWORK APPROACH FOR BICLUSTERING OF GENE EXPRESSION DATA BASED ON THE PLAID MODEL

摘要：

Biclustering techniques, for simultaneous row-column clustering, are widely used in the analysis of the gene expression data. Many different biclustering techniques have been proposed, such as the Iterative Signature Algorithm (ISA) 1, global biclustering 2, evolutionary fuzzy biclustering 3, etc. Among these techniques, the plaid model is often used for multivariate data analysis. However, difficulties exist because there are mixed binary and continuous variables in this model for which the traditionally used optimization algorithms suitable for continuous variables cannot be employed in the realization of the biclustering process. In this paper, a novel neural-network approach is proposed to tackle such a mixed binary and continuous optimization problem in the plaid model. Experiment results show that the accuracy of the biclustering can be significantly improved with the proposed algorithm.

关键词： Biclustering Plaid model Neural network Gene ezpression data analysis

作者: JIN ZHANG JIAJUN WANG HONG YAN

作者单位: School of Electronics and Information Engineering, Soochow University, Suzhou, 215021, China Department of Electronic Engineering, City University of Hong Kong School of Electrical and Informat

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1082-1087

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）