Sentence examples for squared expression from inspiring English sources

For each gene, we calculated the averaged of the squared expression level from the 1,260 experiments as described in a previous study [ 9], and defined the normalized resulting value as transcriptional plasticity, which reflected the dynamic extent of its expression level in various conditions.

The transcriptional or expression plasticity was estimated as the average of the squared log2 expression ratio from over 1000 microarray experiments, which reflects the capacity for a gene to change its transcriptional level under different conditions [ 34].

Transcriptional plasticity was also calculated separately for up-regulation (the average of the squared positive expression level) or down-regulation (the average of the squared negative expression level).

For this purpose, we calculated the log ratio between human-orangutan and chimpanzee-orangutan squared mean expression level differences for each probe set.

The transcriptional plasticity for each gene was quantified as the average of the squared log2 expression ratio.

Results show that the balancing process under the logarithmic formulation converges faster than with the least-squares expression and is also more appropriate to balance the stock allowance for proper machining of the part.

Finally, C is the K ×  n matrix of unknown linear coefficients c ki, which we estimate by minimising the penalised least squares expression Y − ΦC T Y − ΦC + λ C T RC.

Through the ReML optimized weighted-least squares expression, non-zero values for the group parameters are more favored then non-zero random effects; meaning that when possible the model will load weight into the joint-group image over placing an equal weigh onto each of the individual subject perturbation images.

According to this, the squared minimum distance expression for the precoded algorithm is given by d min RA-SFBC 2 ≈ d min 16 - QAM 2 N R L ∑ i = 0 L - 1 g N R, i, k, (A.1).

Diversity for NEX and ENDEX genes in the respective tissues was calculated as the average squared difference in expression between the two biological replicates.

It can be easily seen that the incorporation of operon information leads to a better estimate, with a smaller mean squared error, of expression levels.