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Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach

Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach

George Potamias, Sofia Kaforou, Dimitris Kafetzopoulos
Copyright: © 2013 |Pages: 12
ISBN13: 9781466636040|ISBN10: 1466636041|EISBN13: 9781466636057
DOI: 10.4018/978-1-4666-3604-0.ch088
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MLA

Potamias, George, et al. "Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach." Bioinformatics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 1676-1687. https://doi.org/10.4018/978-1-4666-3604-0.ch088

APA

Potamias, G., Kaforou, S., & Kafetzopoulos, D. (2013). Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach. In I. Management Association (Ed.), Bioinformatics: Concepts, Methodologies, Tools, and Applications (pp. 1676-1687). IGI Global. https://doi.org/10.4018/978-1-4666-3604-0.ch088

Chicago

Potamias, George, Sofia Kaforou, and Dimitris Kafetzopoulos. "Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach." In Bioinformatics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1676-1687. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3604-0.ch088

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Abstract

In this paper, the authors present an assessment of the reliability of microarray experiments as well as their cross-laboratory/platform reproducibility rise as the major need. A critical challenge concerns the design of optimal universal reference rna (urr) samples to maximize detectable spots in two-color/channel microarray experiments, decrease the variability of microarray data, and finally ease the comparison between heterogeneous microarray datasets. Toward this target, the authors present an in-silico (binary) optimization process the solutions of which present optimal urr sample designs. Setting a cut-off threshold value over which a gene is considered as detectably expressed enables the process. Experimental results are quite encouraging and the related discussion highlights the suitability and flexibility of the approach.

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