On the basis of the developed algorithms, software was created for modeling a neural-like parallel hierarchical network, which was used for image classification and fast processing.
Whenever a kinetic equation that involves stoichiometry values is created for models in SBML Level 3 [ 31], the algorithm inserts the ID of the corresponding species reference rather than the actual numerical value of the stoichiometry into the rate law.
Mass and energy balance models have been created for the composting process.
A few mice models has been created for lamin A/C knock out or mutations.
During model registration, a PID file is created for each model.
Twenty imputed data sets were created for each model.
Two sets of data were created for each model in the cross-validation; a training and a validation set.
For example, the AtGenExpress database was created for the model-plant Arabidopsis from a host of tissue preferential and stress response expression profiles [ 1].
Section images and volume renderings were created for each model individually and merged using ImageMagick (on-line resource [ 24]) convert to produce a color image.
Given that we did not collect direct information from non-cases, a simulated individual database was created for each model, taking into consideration information from the population-at-risk over a 5 year period.
