Sentence examples for microarray datasets to from inspiring English sources

We use the microarray datasets to determine whether significant bias is encountered in this gene expression data.

We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response.

Here, we set up a bioinformatics platform by integrating three different microarray datasets to reveal key regulators involved in the metastasis of lung cancer.

Briefly, P-value derived from Wilcoxon rank sum test were calculated for all breast-cancer-study-validated genes in other microarray datasets to check whether significant variation between breast cancer and controls also appeared in those disease datasets.

We established a large-scale meta-analysis of EOC microarray datasets to determine EOC molecular subtypes.

These were then compared in the same way as the Arabidopsis microarray datasets to find overlap.

All the current rice co-expression analysis tools in Table 2 use the whole microarray dataset to construct the network except ROAD and RiceXPro.

Data integration and various bioinformatics approaches may substantially facilitate unmasking of a cryptic microarray dataset to identify targets and to dissect underlying mechanisms.

Annotated Stat3 and c-Myc ChIP-chip regions were compared with a published microarray dataset to evaluate developmental expression patterns of Stat3 and c-Myc bound genes.

Since our approach using optimal principal components was dependent on data structure, such as microarray platform and experimental conditions, we could not directly apply coefficients obtained from our microarray dataset to the other independent microarray datasets.

We applied this approach to a CONSTITUTIVE PHOTOMORPHOGENIC1 (COP1) mutant time course microarray dataset to detect sequence elements that selectively bind to TFs and miRNAs in the process.