Biclustering gene expression data in the presence of noise

Authors:
Ahsan Abdullah;Amir Hussain
Affiliations:
National Univ. of Computer & Emerging Sciences, Islamabad, Pakistan;Department of Computing Science & Mathematics, University of Stirling, UK
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

Citing 2
Cited 0

Discovering local structure in gene expression data: the order-preserving submatrix problem

Proceedings of the sixth annual international conference on Computational biology
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

Production of gene expression chip involves a large number of error-prone steps that lead to a high level of noise in the corresponding data. Given the variety of available biclustering algorithms, one of the problems faced by biologists is the selection of the algorithm most appropriate for a given gene expression data set. This paper compares two techniques for biclustering of gene expression data i.e. a recent technique based on crossing minimization paradigm and the other being Order Preserving Sub Matrix (OPSM) technique. The main parameter for evaluation being the quality of the results in the presence of noise in gene expression data. The evaluation is based on using simulated data as well as real data. Several limitations of OPSM were exposed during the analysis, the key being its susceptibility to noise.