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In this field proof-of-concept, the inversion of the two-dimensional (2D) head dataset was computed with a 2D radial flow algorithm that considers Kh, Kv/Kh, Ss and wellbore storage effects.
However, this indirect TR dataset was computed using non-growing cells [17].
For every condition, the set of correlations between all genes in the respective dataset was computed.
Thirdly, the correlation variability between microarray datasets (Spellman, Hasse, PramilaL, PramilaS) within each normalised dataset was computed, for each gene pair.
First, a indirect TR dataset was computed using genomic mRNA half-life data [17] (in minutes) and the newly computed mRNA amount dataset described above.
A pairwise comparison of each strain's genome against all others in the dataset was computed, to remove highly homologous isolates.
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In order to compute the optimal number of training epochs, the error on validation dataset is computed while the network is being trained.
The values of most of these descriptors for each of the analogs in the dataset were computed using the Dragon software (version 5.3).
The electrostatic/polar solvation free energies for the proteins in the GB dataset were computed by using the GB model implemented in Amber14.
All tracts in the dataset were computed by seeding each voxel with an FA greater than 0.2.
The perturbational content of a normalized dataset is computed by means of a gene expression variability index equal for each gene to the percentage of experiments in which gene expression is an outlier with respect to a confidence interval centered on the mean value and of width equal to twice the standard deviation.
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