There are many machine learning techniques that have been applied for classifying microarray dataset, including SVM, K nearest neighbor (KNN), random forest (RF), artificial neural network (ANN), and naive Bayes (NB).
In Bioinformatics, l1LS sparse coding has been applied for the classification of microarray gene expression data in [ 11].
At present, diverse technologies have already been applied for the detection of SNPs on microarrays.
The lectin microarrays have been applied for fast and high-throughput glycan profiling and comparison for a variety of samples, e.g., glycoprotein [ 22- 26], cell lysates [ 27- 29], clinical specimens [ 30, 31], mammalian cells [ 20, 32- 34], bacteria [ 17, 35] and virus [ 36].
For the first time the microarray methodology has been applied for the simultaneous identification of different mixed population of spoilage yeast and bacteria directly isolated from wine, thus indicating the practicability of oligonucleotide microarrays as a contamination control in wine industry.
Gene expression microarrays can therefore be, and have been, applied for the identification of downstream targets for miRNAs [ 44- 46].
Linear & LOWESS, which is the default normalization method in the Agilent Feature Extraction Software A.7.5.1, were applied for normalizing Agilent microarrays.
The Robust Multichip Average function was applied for normalization of the microarray data [ 74].
Platform-specific preprocessing steps were applied for Affymetrix and cDNA microarrays (detailed in online supplementary materials and methods).
Leukemia was one of the first diseases in which DNA microarray technology was applied for monitoring drug effects by analyzing the effect of ATRA treatment in APL-derived cell lines.
