Biclustering gene expression data in the presence of noise

Abdullah, Ahsan; Hussain, Amir

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.

Citation

Abdullah, A., & Hussain, A. (2005, September). Biclustering gene expression data in the presence of noise. Presented at ICANN 2005: International Conference on Artificial Neural Networks, Warsaw, Poland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICANN 2005: International Conference on Artificial Neural Networks
• Start Date: Sep 11, 2005
• End Date: Sep 15, 2005
• Publication Date: 2005
• Deposit Date: Oct 17, 2019
• Pages: 611-616
• Series Title: Lecture Notes in Computer Science
• Series Number: 3696
• Series ISSN: 0302-9743
• Book Title: Artificial Neural Networks: Biological Inspirations – ICANN 2005 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part I
• ISBN: 978-3-540-28752-0
• DOI: https://doi.org/10.1007/11550822_95
• Keywords: Gene Expression Data; Noise Immunity; Array Technology; Subspace Cluster; Biclustering Algorithm
• Public URL: http://researchrepository.napier.ac.uk/Output/1793658