Luo et al[ 21] also proposed a random matrix theory-based approach to infer transcriptional networks based on microarray data.
Herein, we elaborated a strategy of control gene selection based on microarray data and illustrated it by analyzing endomyocardial biopsies with acute cardiac rejection and infection.
Based on microarray data, we chose 29 genes that showed strong, moderate, and weak expression in xylose rather than glucose fermentation.
The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered.
Of these, elements that are key to functional genomics studies are the protein protein interaction data and meta-expression data based on microarray data analyses.
Therefore, selecting miRNAs based on microarray data may not be a good strategy.
Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only.
Maximal Purg1-pom1 induction was reached within 10 minutes, which was ∼2.2-fold higher than pom1 expression levels driven from its own promoter (based on microarray data).
The mRNA expression levels of H3K4me3 and H3K27me3 marked genes in PC3 and EP156T cells were analyzed based on microarray data.
Our results show that for the considered microarray technologies, cancer types and types of outcome, integrating interactome data improves classification of cancer outcome based on microarray data.
The pom1 expression levels are ∼3.8-fold below average transcript levels [19], and repressed Purg1-pom1 levels were ∼90% of the native pom1 levels (based on microarray data).
