Sentence examples for microarray datasets relative from inspiring English sources

To uncover the pathways against which future therapies could be developed we undertook a meta-analysis of gene expression from five large microarray datasets relative to ER status.

The analysis type 〈〈cancer vs. normal〉〉 was then applied to filter those microarray datasets exploring cancer relative to its normal tissue.

In general, we have experienced that microarray datasets with a low background relative to signal, loess-based normalization, and conservative background subtraction (e.g. SPOT Image Processing) produce standard deviations that are not strongly intensity-dependent.

Since the TGFβ target ANGPTL4 plays a key role in lung metastasis development, we performed a meta-analysis of ANGPTL4 expression relative to WWOX in microarray datasets from breast carcinomas.

In this study, we found that SOX2 mRNA was largely higher in lung SCCs relative to adenocarcinomas from various microarray datasets.

To remove microarray platform/source systematic biases, we applied DWD to the 2 test datasets relative to the combined test set.

We found that SOX2 mRNA levels are significantly higher in lung SCCs relative to adenocarcinomas or large-cell lung carcinomas from various published microarray datasets.

Rhodes et al. [ 10] examined 21 cancer microarray datasets spanning 12 distinct cancer types and identified a set of 67 genes that are universally activated in most cancer types relative to normal tissues.

This enrichment was further validated in the independent microarray datasets (Supplementary Fig. 25).

A scalable method for integration and functional analysis of multiple microarray datasets.

A snapshot of gene expression signatures generated using microarray datasets associated with excessive scarring.