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The wrapper approach, which is popular in many machine learning applications, is not extensively used in DNA microarray tasks, and in most cases the gene selection is performed by ranking genes on the basis of scores, correlation coefficients, mutual information and sensitivity analysis.
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This paper addresses these tasks and presents a new single-chip calibration method for microarrays based on a physical model of hybridization.
Since each single microarray hybridization experiment produces massive amounts of data, handling, processing and analyzing the data become challenging tasks and the application of bioinformatics is absolutely essential.
Stimuli, tasks, and procedure.
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Here, for the sake of comparison, we experiment with both PCA and ICA transformation methods and will report on their efficiency for our gene microarray classification task later on.
The identification of genes with age-dependent DE patterns is the central microarray analysis task of many ageing studies.
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