Assuming that promoter elements driving a weak expression yield fewer transgenic lines with a strong expression this percentage is taken as an approximation of the expression strength of the promoter fragment under consideration.
This approach gives a reasonably accurate approximation of gene expression and was successfully used for studying gene transcription levels and tissue-specific gene expression (for example, [24], [26], [63], [64], [65]).
Using an approximation of this expression, they manage to link the observed outbreaks to classical percolation theory tools.
In 2010, symbol error probability (SEP) of TQAM was evaluated by K. Cho, J. Lee, and D. Yoon for additive white Gaussian noise (AWGN) channel [8] and also, an approximation of SEP expression for TQAM was derived for AWGN and fading channels by T. T. DUY and H. Y. KONG [9], in which maximal-ratio combining (MRC) was used to analyze TQAM over Rayleigh fading channel with multipath reception.
It is a rough first approximation of the expression rate.
Consequently, it provides a better approximation of the expression divergence under neutral evolution between distant species.
For each species, an approximation of the expression level for each domain was obtained by summing up the number of EST reads associated with the specific domain.
We construct a bell-shaped intensity pattern (referred to as CAP-BELL) as an approximation of 0-1 expressindicatorstors in the data, plus white Gaussian noise with standard deviation 0.25.
While a number of publications have presented methods by which one can analyze RNA-Seq data (many of which are reviewed in [ 11]) and account for unknown covariates [ 12- 15], the steps we present herein ultimately provide a straightforward approach that allows for more accurate approximation of gene expression values that can be confidently used in downstream disease-based comparisons.
Our results also suggest that the level of bias introduced into gene expression profiling by amplification is relatively low; expression profiles obtained using aRNA provide a close approximation of the true expression profile of the original sample.
