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The negative validation contained 250 not nucleic acid binding proteins (Shazman and Mandel-Gutfreund, 2008).
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The two data sets (training and validation) containing the 51 variables were used to develop and compare the mathematical models (described in detail in [ 11]).
Even though two of models among 5 cross-validations contain miRNA, methylation, gene, and CNA features, the predictive power was not as good as to the best model with 2 miRNAs and 2 CpG loci.
A selection of model validation is contained in [14, 19, 26 28].
The training and validation datasets contained 445 and 223 cases, and 453 and 227 controls, respectively.
The third validation set contained gene expression data from Affymetrix Hu95 and Hu133 chips (GEO accession number GSE6253).
The 7 validation genes contained within the 49 most DE genes are further highlighted as enlarged triangles in different colors.
To measure the predictive power of the model, cross validation procedure contained the following: parts of the data were kept out of model development, the kept-out parts were predicted by the model and predictions of the kept-out parts were compared with the obtained actual values.
Both validation cohorts contained fewer patients than the development cohort.
Our validation cohort contained samples from a single center eliminating this potential confounding factor.
The microarray platform used by both databases involved in the validation study contained 22,283 probes set.
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