Sentence examples for microarray expression clusters from inspiring English sources

For data from Microarray Expression Clusters resulting from experiments using Affymetrix C. elegans microarrays, the 13,111 probe sets were used as the population for calculating enrichment.

Lists of genes were downloaded from WormBase release WS227 using an AQL (ACEDB Query Language) Query for all Gene Ontology Terms and Microarray Expression Clusters and from the publisher's website for transcription factor binding sites [ 24].

In fact, the Yuan et al. (2007) study mentioned above showed that microarray expression clusters can be predicted as effectively by motif matches for individual TFs, relative to multiple TFs.

To determine the biological processes affected by exposure, we compared this list of differentially expressed genes to lists of genes or probe sets from several data sources including Gene Ontology Terms and Microarray Expression Clusters from WormBase, and transcription factor binding sites from the literature [ 24] (See Additional files 2, 3, 4).

Comparing phylogenetic relationships and microarray-based gene expression clusters it was observed that the following pairs of closely related genes (OsWRKY18 and OsWRKY4 in cluster A, OsWRKY71 and OsWRKY79 in cluster E, OsWRKY100 and OsWRKY53 in cluster I) were co-expressed, reflecting recent duplications and potentially functional redundancy (see Figure 1).

Analyzing microarray expression data using cluster analysis is a common and frequently performed task in functional genomics.

RNA species that vary together under a range of conditions are likely to be under common regulation, and indeed, sets of "co-expressed" genes generated by clustering of microarray expression values have proven useful for identifying potential regulatory elements and transcription factor binding sites [ 1- 5].

This can be useful for discovering relationships between genes, e.g. when analyzing gene clusters from microarray expression data.

Hence, McLachlan et al. [ 47] proposed a mixture model-based approach to the clustering of microarray expression data.

In the field of molecular biology, it has been applied to the clustering of microarray expression data (31, 32), as well as to the spatial probability distribution of protein atoms around a binding ligand (33).

Of these 30 largest S. ratti clusters [ 27], the microarray expression data showed that four were significantly differently expressed between L1 and iL3 stages with a two-fold or greater difference: L1-up SR00012 2.27-fold, SR01068 2.08-fold, SR00026 2.04-fold; iL3-up SR00369 3.60-fold.