Sentence examples for microarray test methods from inspiring English sources

Microarray test methods have proven to be powerful tools for viral identification and subtyping [ 2, 22- 25].

Unfortunately, limited number of samples of microarray datasets in most biological studies makes such statistical test methods ineffective [ 11, 12].

In our previous study [ 17], we compared between the networks of each cancer and its corresponding normal PPI, which were obtained by the AIC (akaike information criterion) order detection and Student t-test methods from microarray expression data of patients and normal people, respectively, to get the PPI differential network in order to reveal PPI alternations during the tumorigenesis process.

In addition, we illustrated the use of appropriate multiple testing methods for microarrays in monitoring differential gene expression between different biological states.

Multiple testing methods normally used in microarray analyses to correct for false positives include FDR (false discovery rate) and Q-value [51] [53].

Our main objective was to study the microarray data derived from particular biological investigations, generated in many different microarray core laboratories, rather than the sets of arrays produced in the context of technology development or testing methods of analysis.

Each of the 3 tested methods performed surprisingly accurately when amplifying from low inputs of total RNA based on microarray analysis validated with QPCR of 37 genes.

There are several testing methods.

This section describes the testing methods necessary to develop a set of external RNA controls that can be used to assess the technical performance of microarray experiments.

We use a logistic regression framework to test for functional enrichment, similar to LRpath (Sartor et al., 2009), an enrichment testing method developed for microarray data.

In addition, traditional microarray analysis methods and gene class testing point to a dual role for pharmacological GC doses in chondrocytes.