Here it should be noted that the setup of the cultivation parameters in the compendium is not fully combinatorial, i.e. not all possible combinations of cultivation parameters are present in the dataset.
Besides setup of these parameters, users can adjust the surface of a molecule and filtering of detected channels.
In this section, the setup of the additional parameters is discussed.
In this section, we first describe the generation of the datasets for training and testing and the setup of the training parameters of the deep networks.
New insights into the ELM model are obtained, and the ELM strategy for the setup of the neurons' parameters can be significantly improved.
As the performance of graph coloring depends on the conflict graph topology, we test algorithms on a large number of different networks for each setup of the input parameters, in order to have network neutral performance results.
Each unique setup of these parameters may then yield a specific response in the target tissue.
