Comput. Biol. Med. - Microarray gene expression: a study of between-platform association of Affymetrix and cDNA arrays.

Tópicos

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Resumo

Microarrays technology has been expanding remarkably since its launch about 15 years ago. With its advancement along with the increase of popularity, the technology affords the luxury that gene expressions can be measured in any of its multiple platforms. However, the generated results from the microarray platforms remain incomparable. In this direction, we earlier developed and tested an approach to address the incomparability of the expression measures of Affymetrix?- and cDNA-platforms. The method was an exploit involving transformation of Affymetrix data, which brought the gene expressions of both cDNA and Affymetrix platforms to a common and comparable level. The encouraging outcome of that investigation has subsequently acted as a motivator to focus attention on examining further in the direction of defining the association between the two platforms. Accordingly, this paper takes on a novel exploration towards determining a precise association using a wide range of statistical and machine learning approaches, specifically the various models are elaborately trailed using-regression (linear, cubic-polynomial, LOESS, bootstrap aggregating) and artificial neural networks (self-organizing maps and feedforward networks). After careful comparison, the existing relationship between the data from the two platforms is found to be non-linear where feedforward neural network captures the best delineation of the association.

Resumo Limpo

microarray technolog expand remark sinc launch year ago advanc along increas popular technolog afford luxuri gene express can measur multipl platform howev generat result microarray platform remain incompar direct earlier develop test approach address incompar express measur affymetrix cdnaplatform method exploit involv transform affymetrix data brought gene express cdna affymetrix platform common compar level encourag outcom investig subsequ act motiv focus attent examin direct defin associ two platform accord paper take novel explor toward determin precis associ use wide rang statist machin learn approach specif various model elabor trail usingregress linear cubicpolynomi loess bootstrap aggreg artifici neural network selforgan map feedforward network care comparison exist relationship data two platform found nonlinear feedforward neural network captur best delin associ

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