The theory is illustrated by numerical experiments computed using the open domain MATLAB code bvpsuite, based on polynomial collocation.
The efficiency of the entire stimulation system for physiological experiments is computed by multiplying the results in sections 4.4.1 and 4.4.2.
Then a MA map for each experiment is computed by summing the probability values for all the foci.
Gene expression for each experiment was computed by log2-base fold changes between treatment and control, or between wild type and mutants.
Gene expression for each experiment was computed by log2 fold changes between treatment and control, or between wild type and mutant.
For each chunk, the coverage of one experiment was computed by translating the coordinates of the chunk in the genomic coordinates of the strain used in this experiment, and counting the number of forward reads having a start position within the chunk.
In this paper, we extend this theorem to the case of multiresponse experiments, and we show that when the number of experiments is finite, the c‐,A‐,T‐ and D-optimal design of multiresponse experiments can be computed by second-order cone programming (SOCP).
For estimating the coefficients, an optimal design consisting of 17 hydrolysis experiments has been computed by Design Expert® (Version 8.0.6, Stat-Ease, inc., 2010).
'Signal log ratio' is the difference in expression level for a transcript between two experiments, and is computed by Affymetrix Microarray Suite Software MAS 5.0.
In the above experiments, each recognition rate is computed by dividing "the rate of correct answers" by "the rate of correct answers" plus "the rate of wrong answers".
