Sentence examples for mining of expression from inspiring English sources

Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set.

Data mining of expression and behavioral data indicates that Comt expression correlates with two aspects of drug response in C57/DBA recombinant inbred strains.

Data mining of expression and behavioral data indicates that both Gnb1 and Comt expression correlate with aspects of drug response in C57/DBA recombinant inbred strains.

Functional mining of expression clusters was then performed by searching for overrepresented GO terms (Gaussian hypergeometric test) in comparison with the whole set of well-measured genes using AMEN software.

Data-mining of expression data from breast cancer patients correlates ZNF217 with reduced overall survival.

Data-mining of expression data from breast cancer patients also associates high ZNF217 expression with poor prognosis across multiple intrinsic subtypes and reduced response to hormone therapy.

In this study, we profiled the expression of hundreds of GPCRs in both undifferentiated and differentiated ES cells using a GPCR-specific real time RT-PCR microarray specific for 343 GPCRs and data mining of RNA expression libraries.

This tool makes it possible to efficiently integrate the workflow going from the analysis to the mining of microarray expression datasets in an automated and seamless way.

In this study, we introduce a novel subject-specialized database, namely the tissue-specific genes database (TiSGeD), which particularly provides information of tissue-specific genes derived from literature and data mining of gene expression profiles.

In addition, post hoc mining of the expression dataset showed that another SNP (rs10242920 - ELMO1) association that was consistent with the effect reported in the CGEMS dataset was also actually associated with altered expression in carriers, albeit with less significance (P = 0.005) than originally used for gene and SNP selection.

Originally applied to Arabidopsis stress responses, detailed informatics mining of gene expression profiling data and targeted metabolite measurements induced by deficiency of sulfur and nitrogen led to the identification of previously unknown molecular players involved in glucosinolate biosynthesis (Hirai et al. 2005).