Sentence examples for microarray analysis many from inspiring English sources

In order to determine the gene signature for response prediction from microarray analysis, many researchers have tried to develop and apply the most accurate classification algorithms.

Using microarray analysis, many laboratories have reported the differential expression of several novel genes associated with the progression of pancreatic cancer (Argani et al, 2001; Han et al, 2002; Terris et al, 2002).

Although many laboratory-based animal or cell-culture studies have investigated the effects of arsenic exposure on gene expression using microarray analysis, many of these were conducted on arsenic exposures outside the normal range of U.S. contamination, and only a few have studied arsenic-exposed humans (Andrew et al. 2007; Argos et al. 2006; Lu et al. 2001; Wu MM et al. 2003).

Regarding the results of our microarray analysis, many adhesion enzymes were much more highly expressed in SKOV3-NC compared with OVCAR8-NC cells, that is, the integrins ITGα2, ITGβ3, ITGβ6 as well as the glycosylation enzymes GCNT3, ST3GAL6 and ST6GAL1 (data not shown).

Microarray analysis revealed many novel genes differentially expressed by ventilation including matrix metalloproteinase-8 (MMP8) and GADD45α.

Nevertheless, microarray analysis shows many genes over-expressed in the uterus when compared to either the magnum or white isthmus.

41 Chromosomal microarray analysis identifies many common causes of ASDs, such as the maternal duplication in chromosome band 15q11 q13; microdeletions and microduplications in 16p11.2, 22q11, and 22q13 deletion syndromes; Potocki-Lupski syndrome; and some cases with Angelman syndrome.

MiRNAs scored as either present (Ct<27) or absent were then compared with microarray data; 75 of the miRNAs chosen for validation were also scored as being present by microarray analysis and many of these are indicated in Figure 5 for comparison.

Interestingly, only a small subset of genes was found to be differentially expressed by microarray analysis and many of these have been implicated in controlling apoptotic pathways (i.e. Erdr1, Ier3, Pdap1, Calm2, Hspca, Mtch1, and Ifitm1 and 2; Table 1).

In microarray data analysis, many gene selection methods have been proposed to reduce the data dimensionality [ 5].

Based on microarray analysis, we identified many differentially expressed genes that involved in carbohydrate and lipid metabolism.