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We analyzed gene-expression data from six microarray datasets containing up to 12 classes of differentiated thyroid pathologies and normal thyroid tissue.
In our study, we analyzed six publicly available microarray datasets containing 347 thyroid tissue samples and defining as many as 12 classes of thyroid tumors.
A recent study involving 3 independent breast cancer microarray datasets containing a total of 699 patients revealed that CEP55 and FOXM1 are amongst the signature prognostic markers which predicts breast cancer outcome [41].
Nine microarray datasets containing normal tissue samples from previously published studies were collected from the Gene Expression Omnibus (GEO).
All microarray datasets containing values higher than 25 were log transformed, under the assumption this data was non-log-transformed data.
Due to the relatively short observation period in our patient cohort, we used large published microarray datasets containing gene expression data linked to clinical and prognostic information in NSCLC patients.
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All experiments were based on goldfish sampled at different seasonal time points; thus, these microarray datasets contain important seasonal gene expression information (Table S1).
On the other hand, since microarray datasets contain a small number of samples and a large number of genes, it is usually desirable to identify small gene subsets with distinct pattern between sample classes.
The complete microarray dataset containing 34 samples from the inner and outer cortex, inner and outer medulla, papillary tips, and renal pelvis and from glomeruli is available at https://www.med.stanford.edu/jhiggins/Normal_Kidney/index.shtml.shtml
We apply our mining approach to an integrated yeast microarray dataset containing 500 yeast experiments (see METHOD) and calculate the ΔAIC value for each gene in the dataset.
Following the general simulation setup of Broët et al. [ 24], we generate a microarray dataset containing 300 miRNAs and five repeat arrays under two conditions.
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