Subspace Clustering of DNA Microarray Data: Theory, Evaluation, and Applications

Subspace Clustering of DNA Microarray Data: Theory, Evaluation, and Applications

Alain B. Tchagang (Information and Communications Technologies, National Research Council of Canada, Ottawa, Canada), Fazel Famili (Information and Communications Technologies, National Research Council of Canada, Ottawa, Canada) and Youlian Pan (Information and Communications Technologies, National Research Council of Canada, Ottawa, Canada)
DOI: 10.4018/IJCMAM.2014070101
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Abstract

Identification of biological significant subspace clusters (biclusters and triclusters) of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Several methods and applications of subspace clustering (biclustering and triclustering) in DNA microarray data analysis have been developed in recent years. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. This review discusses and compares these methods, highlights their mathematical principles, and provides insight into the applications to solve biological problems.
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2. Dna Microarray

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 and triclustering techniques.

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