Sentence examples for best gene expression from inspiring English sources

A ubiquitin isogene showed the best gene expression stability as a single reference gene, while a 3-gene combination of another ubiquitin and two cyclophilin isogenes was the best reference gene combination.

Cluster validity testing suggested that the best gene expression clustering occurred with 2 or 50 clusters depending on the distance measure and clustering method used (Table  4).

Clinical and demographical confounders did not improve the performance of the best gene expression based FLP and FTP (see Methods for the exact list of Clinical and demographical confounders we checked).

We found that, not surprisingly, the best gene expression based surrogates for the abundance of a particular cell subset were the expression of the genes associated with the characteristic cell surface phenotype (e.g. CD8, CD3D/G, CD20), or other genes known to be associated with cell-type specific functions (such as cytotoxicity or immunoglobulin secretion).

We used the same 5 gene-set to find the best gene expression combination discriminating the paternal Q from q haplotypes (corresponding to the whole confidence interval of the GGA5 AF QTL) and used it to predict the Q versus q mutation received by the recombinant animals.

Second, using the transcriptome profiles for the 5 genes previously detected as the best possible signature of the location of Q versus q GGA5 AF QTL mutation, we looked for the best gene expression combination discriminating the 16 Q from the 16 q animals using two methods, g.e.e

Using the ribosomal proteins as an example, we also show that translational, rather than transcriptional, feedback best coordinates gene expression during assembly of macromolecular complexes.

Therefore, an important question pertains to which assumption best describes gene expression data.

Cultures resuspended to OD600 = 0.2 0.5 resulted in the best transient gene expression activity.

Considering all sixteen genes, the ZNF domain sequence similarity correlates best with gene expression similarity (0.42).

With appropriate fitness function, the evolutionary algorithm is able to identify the optimal network structure that fits best the gene expression data, where the consistence between network topology and gene expression data is evaluated with Boolean network based on certain rules.