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Protein three-dimensional model quality throughout the modelling process was assessed by MetaMQAP22, a programme that predicts the global accuracy of the protein structural model and deviations of individual residues from the positions of their counterparts in the true (unknown) structure.
The modelling process was accomplished using adaptive neuro-fuzzy inference system (ANFIS) and polynomial modelling approaches.
A two-stage modelling process was carried out to account for the presence absence and cover abundance of PFTs separately.
Due to the high combinatorial nature of the problem, the modelling process was divided into four phases: estimation of the potential demand, definition of stops, determination of the service O/D matrix and establishment of the most profitable routes.
Therefore, continuing this modelling process was pointless.
The modelling process was repeated for the following hypothetical scenarios, representing outcomes from possible management options.
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Although recent crop modelling frameworks can technically support model building, the modelling process is not always well documented and difficult to repeat.
Second, a semantic model for describing the modelling process is proposed, and its instantiation is studied.
Structure that was obtained by the modelling process is indicated as 'Model (D1-EM-D2)' and crystal structures as 'Crystal', with its respective PDB ID, which is linked to the PDB structure into the RCSB Protein Data Bank.
Multiresponse modelling (taking more than one reactant and product into consideration in the modelling process) is a powerful tool to model complicated consecutive and parallel reactions, like the Maillard reaction.
However, with time-series data, the modelling process is complicated by the need to model not only the interdependence between the series, but also the serial dependence within the component series [ 17].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com