Sentence examples for microarray datasets including from inspiring English sources

In order to relate the A17 phenotype with that of other mesenchymal phenotypes involved in tumorigenesis, we analyzed the enrichment of these A17 Signatures in a compendium of 360 publicly available human whole-genome microarray datasets, including samples of MSCs, breast stroma, breast cancer and varied types of sarcoma (Figure 3b).

Similar results were observed using two other breast tumor microarray datasets, including a University of North Carolina dataset (GSE18229) that includes normal mammary tissue and the Miller et al., 2005 (GSE3494) dataset consisting of primary invasive breast tumors (Fig. S1A and data not shown) [4], [20].

Phenotypic microarray datasets including antibiotic resistances were compared with the functional predictions made from the annotated N. cyriacigeorgica GUH-2 genome.

Two breast cancer Affymetrix HG-U133A microarray datasets including patient outcome information were downloaded from the National Center for Biotechnology Information GEO data repository.

After preprocessing the microarray datasets including the above normalization, some DEGs were selected using the R package "SAMR" (nperms(Number of permutations used to estimate false discovery rates) = 100; del (Value of delta to define cutoff rule) = 2.5).

We also performed searches in public repositories of microarray datasets, including the National Centre for Biotechnology Information (Gene Expression Omnibus), the European Bioinformatics Institute (ArrayExpress), and the Centre for Information Biology Gene Expression Database (CIBEX).

Genes showing the strongest responses in both RNA-Seq and microarray datasets included the typical AhR responsive genes such as Cyp1a1, Cyp1a2, Nqo1, and Tiparp (Additional file 2).

Considering that the 8 gene microarray datasets include different characteristics in terms of number of samples, genes, classes and the type of the cancer to which these data is related to; overall, ICA-based RotBoost classifier seems to be more effective than PCA-based RotBoost.

Here we present a summary of the microarray dataset, including analysis of tissue-specific gene expression profiles and associated expression profiles of relevant metabolic pathways.

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).

We have deposited the entire microarray dataset including exponential and stationary phase at Gene Expression Omnibus (GEO, ) database with the accession number of GSE10302 so interested parties can conduct their analyses.