Sentence examples for microarray experimental approaches from inspiring English sources

In the present study, we have integrated computational bioinformatics and microarray experimental approaches to classify the tissue specificity and developmental distribution of mouse retina transcripts.

In this study, we have integrated computational bioinformatics and microarray experimental approaches to classify the tissue specificity and developmental distribution of mouse retina transcripts.

Although the genes appeared in the same order with regard to the magnitude of the effect, the real time data resulted in higher expression ratios, an effect that may be due to signal saturation in the microarray experimental approach.

Identifying sources of variation in expression microarray data and the effect of variance in gene expression measurements on complex predictive and diagnostic models is essential when translating microarray-based experimental approaches into clinical assays.

Using a microarray based experimental approach we have identified a number of putative miR-143 targets that are down-regulated at the transcript level by miR-143 overexpression and contain miR-143 seed sites in their 3'UTRs.

In this study, based on microarray technology and experimental approaches, we suggest that NLC1-C functions as a repressor to regulate the expression of miR-320a and miR-383 by binding to Nucleolin in the nucleus and in the cytoplasm, NLC1-C is the direct target of miR-320a and miR-383 in human spermatogenesis.

VLK and MCF designed the microarray and the experimental approach, VLK performed the microarray analysis and data interpretation, TS generated and validated the VSG RNAi line and SKAN performed immunochemical analysis of the Rab5 overexpressor lines.

Using a combination of computational (selection of candidate TF markers and malignancy prediction) and experimental approaches (tissue microarray and western blotting on patient samples) we identified and evaluated E2F5 transcription factor involved in cell proliferation, as a promising candidate regulatory target in early stage disease.

Additionally, with the rapid increase in the last few years in the validation of regulatory relationships using experimental approaches such as microarray, deep sequencing and ChIP-seq [ 19- 21], these high quality data will no doubt contribute to the construction of networks.

The sequenced MAP K-10 and MAH 104 genomes have been previously compared with both bioinformatic and experimental approaches, including DNA microarrays [ 4, 25- 28].

These results are in agreement with those results obtained from the promoter-probe experiment (Figure 2B), validating the experimental approaches used in the microarray and qPCR experiments.