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Microarray based gene expression dataset considered for the present study consists of a total of 39 samples of the same WT condition, with 3923 genes in each sample [ 37].
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C. elegans: In addition to comparisons of gene sets either individually or collectively to themselves, these phenotype gene sets are useful in analysis of microarray based gene expression datasets in worm and fly.
We evaluated an independent and publicly available microarray-based gene expression dataset (GSE40115) that includes breast tumors from 32 patients with basal-like disease (20 with BRCA1 germline mutations and 12 with sporadic tumors [i.e. unknown BRCA1 status]).
Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format.
The TCGA dataset contains microarray based gene expression data from over 500 high-grade ovarian cancer samples.
Functional annotation and biological inference based on a single gene expression dataset has limitations due to experimental noise [ 4].
The results based on the oxidative stress gene expression dataset [ 22] are not consistent with the results based on the other three gene expression datasets.
The human gene expression dataset was based on the Affymetrix HG_U133 chip.
The mouse gene expression dataset was based on Q-PCR experiments, performed by Bookout et al., [ 2], for all 49 mouse NR genes, over 39 normal tissues, repeated in two mouse strains (C57Bl/6J & 129x1/SvJ).
A number of methods, based on microarray gene expression datasets, have been proposed for measuring the tissue specificity of gene use.
Microarray based breast cancer gene expression datasets usually consist of a small number of samples due to the fact they are time consuming and expensive, which has always been an issue for accurately categorizing breast cancer prognosis groups [ 18].
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