Sentence examples for microarray dataset demonstrated from inspiring English sources
Application to a colon cancer microarray dataset demonstrated that the colon cancer-specific gene network captured the most important genetic interplays in several cellular processes such as differentiation, mitogenesis, proliferation, apoptosis, inflammation and immunity that are known to be pivotal for tumorigenesis.
Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods.
Evidence from expressed sequence tag, cDNA, genome-wide tiling and splicing microarray datasets demonstrate that alternative splicing occurs in >90% of genes [1] [3].
For a large Arabidopsis thaliana microarray dataset, we demonstrate here that gene selection, based on the study of principal components, proposed genes typical for either IAA-or pathogen-associated contrasts.
Using TCGA GBM microarray datasets, we demonstrated that regulation between ceRNAs is dynamic, however the optimal conditions are quantifiable.
The general application of the maximum likelihood (MLHD) classifier implemented in a genetic algorithm to microarray datasets was demonstrated by Trevino and Ooi [ 25, 26].
The quality of the microarray datasets was demonstrated by verifying reproducibility among replicates by hierarchical cluster analysis using Pearson correlation and average linkage.
After reapplying the same set of analyses to eight real-life multiclass microarray datasets, we demonstrate how the DDP works for datasets with different number of classes.
The Finak microarray dataset provided new data demonstrating a clinical relevance for RNA expression of CXCL1 in the stroma.
This microarray dataset was also validated by demonstrating that the top 8 genes most differentially expressed between the proximal and distal colon were also 100% consistently differentially expressed using real time RTPCR (as shown in Additional file 1: Table S3).
Finally, we used a microarray dataset for yeast Saccharomyces cerevisiae that demonstrated the scalability and precision of QAPgrid and its possibility as a novel tool for functional genomics.
