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First, the datasets were normalized using quantile normalization to ensure that inherent large-scale expression differences in the datasets based on different sources and laboratories were minimized.
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The dataset was normalized using the RMA algorithm [ 25].
The number of reads per protein coding mRNA was determined using Partek Genomics Suite (Partek Inc., St . Louis MO, USA) and the dataset was normalized using the trimmed mean of the M-values method [ 19].
In each test, the raw datasets were normalized using the Affymetrix detection algorithms in the MAS5 library and the background levels and PM/MM ratios were corrected according to the Affymetrix Statistical Algorithms.
The human and mouse datasets were normalized using the Robust Multichip Average (RMA) algorithm.
Obtained raw datasets were normalized using the limma package in R/Bioconductor and loess-smoothed over a 300-Kb window size.
Pooled paired-end reads from all sequenced datasets were normalized using Diginorm[ 105] with default parameters for single-pass normalization.
All gametophytic and sporophytic datasets were normalized using freely available dChip 1.3 software [ 41].
Both control and leukemia expression datasets are normalized using quantile normalization (Amaratunga and Cabrera, 2001).
For the 36 cancer datasets generated from Affymetrix microarray platforms, the log2 intensities in each gene expression dataset were normalized using the GC-RMA normalization method (15).
Before the actual learning, the training dataset was normalized using the min max method.
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