Data Mining and Meta-Analysis on DNA Microarray Data

Data Mining and Meta-Analysis on DNA Microarray Data

Triantafyllos Paparountas, Maria Nefeli Nikolaidou-Katsaridou, Gabriella Rustici, Vasilis Aidinis
Copyright: © 2013 |Pages: 41
ISBN13: 9781466636040|ISBN10: 1466636041|EISBN13: 9781466636057
DOI: 10.4018/978-1-4666-3604-0.ch062
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MLA

Paparountas, Triantafyllos, et al. "Data Mining and Meta-Analysis on DNA Microarray Data." Bioinformatics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 1196-1236. https://doi.org/10.4018/978-1-4666-3604-0.ch062

APA

Paparountas, T., Nikolaidou-Katsaridou, M. N., Rustici, G., & Aidinis, V. (2013). Data Mining and Meta-Analysis on DNA Microarray Data. In I. Management Association (Ed.), Bioinformatics: Concepts, Methodologies, Tools, and Applications (pp. 1196-1236). IGI Global. https://doi.org/10.4018/978-1-4666-3604-0.ch062

Chicago

Paparountas, Triantafyllos, et al. "Data Mining and Meta-Analysis on DNA Microarray Data." In Bioinformatics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1196-1236. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3604-0.ch062

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Abstract

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

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