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

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

Alain B. Tchagang, Fazel Famili, Youlian Pan
Copyright: © 2014 |Volume: 4 |Issue: 2 |Pages: 52
ISSN: 1947-3133|EISSN: 1947-3141|EISBN13: 9781466653306|DOI: 10.4018/IJCMAM.2014070101
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MLA

Tchagang, Alain B., et al. "Subspace Clustering of DNA Microarray Data: Theory, Evaluation, and Applications." IJCMAM vol.4, no.2 2014: pp.1-52. http://doi.org/10.4018/IJCMAM.2014070101

APA

Tchagang, A. B., Famili, F., & Pan, Y. (2014). Subspace Clustering of DNA Microarray Data: Theory, Evaluation, and Applications. International Journal of Computational Models and Algorithms in Medicine (IJCMAM), 4(2), 1-52. http://doi.org/10.4018/IJCMAM.2014070101

Chicago

Tchagang, Alain B., Fazel Famili, and Youlian Pan. "Subspace Clustering of DNA Microarray Data: Theory, Evaluation, and Applications," International Journal of Computational Models and Algorithms in Medicine (IJCMAM) 4, no.2: 1-52. http://doi.org/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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