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Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods

Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods

George Sakellaropoulos, Antonis Daskalakis, George Nikiforidis, Christos Argyropoulos
Copyright: © 2009 |Pages: 18
ISBN13: 9781605660769|ISBN10: 1605660760|EISBN13: 9781605660776
DOI: 10.4018/978-1-60566-076-9.ch012
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MLA

Sakellaropoulos, George, et al. "Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods." Handbook of Research on Systems Biology Applications in Medicine, edited by Andriani Daskalaki , IGI Global, 2009, pp. 221-238. https://doi.org/10.4018/978-1-60566-076-9.ch012

APA

Sakellaropoulos, G., Daskalakis, A., Nikiforidis, G., & Argyropoulos, C. (2009). Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods. In A. Daskalaki (Ed.), Handbook of Research on Systems Biology Applications in Medicine (pp. 221-238). IGI Global. https://doi.org/10.4018/978-1-60566-076-9.ch012

Chicago

Sakellaropoulos, George, et al. "Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods." In Handbook of Research on Systems Biology Applications in Medicine, edited by Andriani Daskalaki , 221-238. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-076-9.ch012

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Abstract

The presentation and interpretation of microarray-based genome-wide gene expression profiles as complex biological entities are considered to be problematic due to their featureless, dense nature. Furthermore microarray images are characterized by significant background noise, but the effects of the latter on the holistic interpretation of gene expression profiles remains under-explored. We hypothesize that a framework combining (a) Bayesian methodology for background adjustment in microarray images with (b) model-free modeling tools, may serve the dual purpose of data and model reduction, exposing hitherto hidden features of gene expression profiles. Within the proposed framework, microarray image restoration and noise adjustment is facilitated by a class of prior Maximum Entropy distributions. The resulting gene expression profiles are non-parametrically modeled by kernel density methods, which not only normalize the data, but facilitate the generation of reduced mathematical descriptions of biological variability as mixture models.

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