Sentence examples for microarray data identifies from inspiring English sources

Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature.

Gene Set Enrichment Analysis (GSEA), applied to microarray data, identifies coordinated changes in expression among a priori sets of genes associated with biological features or processes [ 27, 28].

Results presented herein, similar to microarray results recently reported for the lacrimal gland [ 9], indicate that HPCluster analysis of the microarray data identifies multiple sets of genes whose associated pathways correlate with concepts currently hypothesized to explain the early pathophysiological processes and subsequent autoimmunity.

The t-test on microarray data identified miRNAs that were differentially expressed between sepsis and control patients (p<0.01, FDR<0.09).

Similar analysis of the custom/Agilent microarray data identified 6311 T-DMRs across chromosomes 13, 18 and 21[24].

These genes were cross referenced with published microarray data identifying circadian variation in gene expression [23], [24], [25], [26], [27].

Comparison of active versus inactive SLE microarray data identified a total of 30 central nodes; in contrast comparison of SLE versus healthy controls revealed only 19 central nodes.

Our microarray data identified several genes that are involved in the invasion of cancer cells, but knockdown of CD10 expression did not affect the expressions of these genes.

Statistical analyses of the microarray data identified the presence of DNA methylation changes associated with the 16 different subtypes of HNs under study.

Two-way ANOVA of microarray data identified 103 genes that were significantly (>2 fold) modulated by the implant placement and vitamin D deficiency.

A hierarchical clustering of the microarray data identified two clusters of samples, shown in Figure 1 and in dendrogram form in Figure 2, a PC group and a healthy control group.