Sentence examples for microarray dataset including from inspiring English sources

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.

On this basis, to determine the candidate target genes directly regulated by miR-128a, miR-196a and miR-142-3p miR-142-3p miR-142-3ptasets: the list of 1981 total target genes predicted for the 3 miRs (dataset 1) and a microarray dataset including 676 genes (725 probes) modulated in all HCV clones as compared to Huh-7 cells (dataset 2) reported in our previous study [ 21].

The microarray dataset includes 43 samples hybridized on an Agilent Human miRNA Microarray 1.0 coming from nine different human tissues (brain, breast, heart, liver, placenta, testis, ovary, skeletal muscle, and thymus).

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].

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).

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

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

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).

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).