Adaptive signal processing
Machine Learning
Self-Organizing Maps
The Grammar of Graphics (Statistics and Computing)
The Grammar of Graphics (Statistics and Computing)
Bagging multiple comparisons from microarray data
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
ggplot2: Elegant Graphics for Data Analysis
ggplot2: Elegant Graphics for Data Analysis
Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis
IEEE Transactions on Information Technology in Biomedicine
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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^(R)- 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.