Biclustering of DNA Microarray Data: Theory, Evaluation, and Applications

Biclustering of DNA Microarray Data: Theory, Evaluation, and Applications

Alain B. Tchagang (National Research Council, Canada), Youlian Pan (National Research Council, Canada), Fazel Famili (National Research Council, Canada), Ahmed H. Tewfik (University of Minnesota, USA) and Panayiotis V. Benos (University of Pittsburgh, USA)
Copyright: © 2013 |Pages: 39
DOI: 10.4018/978-1-4666-3604-0.ch029
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In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed.
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Biclustering Of Dna Microarray Data

Quantitative gene expression measurements using microarrays were first performed by Schena et al. (1995) on 45 Arabidopsis thaliana genes and shortly after, on thousands of genes or even a whole genome (DeRisi et al., 1996; DeRisi et al., 1997). Since that time, various methods for the analysis of such data have been developed. This includes the biclustering techniques.

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