Sentence examples for microarray datasets related from inspiring English sources

Each of six human tumor types (prostate, breast, lung, colon, ovary, and pancreas) was used in the 〈〈profile search〉〉 function in the Oncomine database to find the available microarray datasets related to the specific cancer type.

> We collected and analyzed four microarray datasets related to human diseases (Table 4).

Supplementary data 3 Microarray datasets related to pathogen resistance in Arabidopsis.

To assess the frequency with which cytokine genes are identified as present or absent in human kidney we performed a meta-analysis of publicly available (GEO) Affymetrix microarray datasets related to human kidney tissue.

Five microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity.

To reveal the function of statin induced miRNA in atherosclerotic plaque, we analyzed four microarray datasets related to atherosclerosis from GEO database, and the alteration of pathways in the atherosclerotic lesion group were selected by enrich analysis of significant upregulated gene (Additional file 5: Table S3).

We examined both in-house and publicly available breast cancer DNA microarray datasets relating to invasion and metastasis, thus identifying a cohort of candidate progression-associated biomarkers.

The Arabidopsis microarray dataset related to pathogen resistance was obtained by downloading GEO datasets, which are publicly available at the National Center for Biotechnology Information (NCBI) website (http://www.ncbi.nlm.nih.gov/).nih.gov/

We screened the whole ArrayExpress database and downloaded whole transcript microarray experiment datasets related with folliculogenesis from 18 mouse models by using Affymetrix GeneChip Mouse Genome 430 2.0 platform.

SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process.

With the rapid increase in the size of microarray datasets, identifying functionally related genes from large and diverse microarray gene expression datasets is a key challenge.