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

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Author(s): 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: 2011
Pages: 39
Source title: Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications
Source Author(s)/Editor(s): Limin Angela Liu (Shanghai Jiao Tong University, China), Dongqing Wei (Shanghai Jiao Tong University, China), Yixue Li (Shanghai Jiao Tong University, China)and Huimin Lei (Shanghai Jiao Tong University, China)
DOI: 10.4018/978-1-60960-491-2.ch007

Keywords: Bioinformatics / Biologically-Inspired Computing / Medical Information Science Reference / Medicine & Healthcare

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Abstract

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: Theory, Evaluation, and Applications

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