Sentence examples for microarray data they from inspiring English sources

Due to the structural feature of microarray data (they are represented as arrays of numeric values), M-CLUBS is suitable for analyzing them since it is designed to perform well for Euclidean distances.

Therefore, when applied to microarray data they give very similar overall results [ 9, 10].

However, together with microarray data, they provide useful information for functional deduction of transcription regulators.

While traditional clustering methods, such as hierarchical and K-means clustering, have been shown useful in analyzing microarray data, they have some limitations.

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

Although QPCR results showed higher fold changes than the microarray data, they supported the microarray data as to direction of change for the majority of the genes tested.

In contrast, CREB1 and ATF-4 are the top-scoring transcriptional regulators with respect to microarray data but they do not receive any score based on the iTRAQ data.

In addition, SVMs are very effective classification techniques for microarray data and they significantly improved the classification accuracy performance.

Such heat maps are, for example, commonly used to display microarray data as they quickly show which genes (rows) are differentially expressed under some conditions (columns).

The expression pattern for the remaining 6 genes also qualitatively matched to microarray data, though they were not quantitatively matched (Table 2).

More recently, Price et al. [ 37] have revisited the question of gene-strand bias in bacteria, using gene expression microarray data, and they have shown that, in B. subtilis and E. coli, the genes in operons on the leading strand DNA are more highly expressed than genes in operons on the lagging strand.