Biclusterinig gene expression data in the presence of noise

Citation

Abdullah A & Hussain A (2005) Biclusterinig gene expression data in the presence of noise. In: Duch W W, Kacprzyk J, Oja E & Zadrozny S (eds.) Artificial Neural Networks: Biological Inspirations – ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part I. Lecture Notes in Computer Science, 3696. Berlin Heidelberg: Springer, pp. 611-616. http://link.springer.com/chapter/10.1007/11550822_95#

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.