Sentence examples for microarray data enable from inspiring English sources

The microarray data enable us to identify differentially expressed genes [ 15], discover cancer associated gene signatures, and classify tumors into different subtypes [ 16, 17], which have dramatically advanced our understanding about cancer.

Integration between RNA-seq and microarray data enabled a high-throughput exploration of hidden processes essential in growth and survival of microscopic mussel larvae.

We have demonstrated that ER expression status determined from microarray data enables more accurate determination of clinical outcome of breast cancer in multiple reference cohorts.

Combining information on QTL with microarray data enables genes underlying genetically complex traits to be linked to QTL regions.

The combination of miRNA and mRNA microarray data enabled the identification of miRNAs which potentially act in this developmental process.

The microarray data enabled us to identify 634 genes whose expression response to estrogen treatment required SRC-1, which are hereto referred to as SRC-1-sensitive genes.

Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism.

In another approach, large-scale correlation analysis of public microarray data enabled the in silico identification of genes whose expression is strongly aligned with expression of specific members of the Arabidopsis cellulose synthase (CesA) gene family that are believed to be predominantly involved in either primary or secondary cell wall biogenesis [ 13].

This strategy for analyzing alternative splicing in microarray data will enable delineation of the diversity of splicing in rice.

Several approaches have been used for meta-analysis of microarray data to enable comparative analyses across multiple datasets, to minimize noise and to generate multivariate metrics for clinical use.

Recent international efforts to establish and maintain public databases of Arabidopsis microarray data have enabled the utilization of this data in the analysis of various phytohormone responses, providing genome-wide identification of promoters targeted by phytohormones.