Sentence examples for microarray data followed from inspiring English sources

Then we explain our method in detail, and compare it to two-stage analysis of microarray data, followed by a case study where the method is applied to an analysis of low back pain.

Although we failed to detect global autophagy gene induction by TFEB, analysis of the microarray data followed by qRT-PCR revealed that expression of phosphatase and tensin homolog (PTEN), a lipid phosphatase that antagonizes the phosphatidylinositol-3-kinase (PI3K -Akt-mTOR PI3K -Akt-mTOR PI3K -Akt-mTOR2003), wasignalingcantly elevated in TFEB-treated WT and Tau mice (Fig 8A).

The GUDMAP microarray data follow the same MIAME-compliant conventions as used in GEO (Barrett et al., 2009) and all entries are secondarily submitted to GEO.

Although these results could be explained by cross-reactivity [35], [36], the fact that the allergen microarray-generated data followed these patterns is quite surprising.

k-means clustering of the TNF stimulated time course microarray gene expression data, followed by functional enrichment analysis identified three biologically informative clusters related to apoptosis, cellular proliferation and angiogenesis.

To corroborate the AMFR microarray data, we followed up by Western blot analysis.

The QRT-PCR expression of the selected genes was in accordance with microarray data and followed the same trends in the individual lines analyzed.

Expression profiling of members of large gene families using publicly available data (for, e.g. EST, microarray, MPSS and RNA-seq data), followed by validation of the expression pattern of selected genes using qRT-PCR, is a valuable approach, which provides preliminary indications about the function of newly identified genes and often been recently exploited.

MAGE-ML, MAGE-TAB and MINiML are formats for sharing microarray data; they follow the MIAME guidelines (Brazma et al., 2001).

After constructing equation (1) for the PPI model of each protein in the candidate PPIN, we used the maximum likelihood estimation method [ 20] to identify the association parameters in (1) by microarray data as follows (see Additional file 1): (2) x i (n ) = ∑ j = 1 M i α ^ i j x j (n ) + w i (n ) where α ^ i j is identified by the maximum likelihood estimation method.

Hierarchical clustering of gene expression microarray data is often followed by a study to find subtrees with over-representation of genes annotated to some specific Gene Ontology term or pathway [ 22].