Sentence examples for microarray approach comparing from inspiring English sources

This prompted us to design a whole genome microarray approach comparing gcm gain-of-function and, for the first time, gcm loss-of-function genetics to wildtype in time course experiments along embryogenesis.

In a previous work, we have profiled the miRNA signature during HUVECs aging using a DNA Microarray approach, comparing samples from young cells (population doubling levels (PDLs) 8) versus old cells (PDLs44), according to an established endothelial senescence model.

The aim of this study is to investigate the genomic profile of KITWT/ PDGFRAWT/ SDHWT/ RAS-PWT GIST, by using a massively parallel sequencing and microarray approach, and compare it with the genomic profile of other GIST subtypes.

We reasoned that RNA transcripts with nuclear roles like XIST or comprising the poly(A) component of SC35 domains could be identified using a simple microarray approach to compare nuclear and cytoplasmic RNA populations.

To search for toxin-related or toxicity-influencing genes not readily apparent from EST library annotation, we used a microarray approach to compare the transcriptomes of toxic and non-toxic A. minutum clones and thus identified differences in gene expression potentially linked to toxin synthesis and/or regulation.

The increased power of the RNA-seq approach compared with previous microarray-based approaches enabled the identification of genes that were not highly expressed but that were regulated in response to light.

Overall, fold change was lower when the microarray approach was used compared to real-time PCR (Table 2).

The aim of the present study was to assess the benefit of a disease-specific transcriptome-based profiling approach compared to a generic genomic-based microarray.

The " Standard analysis" NetworkComparer approach compares a gene of interest with other genes (represented by microarray probe sets) belonging to the PFAM family of the query.

We performed experiments on 12 highly imbalanced microarray datasets using linear and Gaussian kernel, achieving the highest average predictive performance with our approach compared with the most well-known feature selection strategies.

Our approach compares gene expression alterations in human disease with expression patterns in publicly available collections, or compendia, of microarray data.