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Quadratic finite element methods offer some advantages for the numerical solution of partial differential equations (PDEs), due to their improved approximation properties in comparison to linear approaches.
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Appealing features of their framework in comparison to linear programming approaches include the absence of a hypothesized objective function, the tolerance to model mis-specifications, as well as the assessment of the probability of a particular solution.
The new design approach resulted into improvement of activity and selectivity in comparison to linear peptides and allowed us to better understand the influence of structural amphipathicity on biological activity.
Most remarkable on the presented approach in comparison to other linear approaches is the additive objective function, which optimizes both the maximum utilization as well as the average utilization.
Obtained results indicate a higher accuracy and generalization performance of the optimal ANN model in comparison to non-linear regression approaches.
In comparison to cloning-based approaches, using a sequence-only approach further reduces the amount of bench work required.
This approach completely neglects non-linear interactions at later stages of the transition development and therefore provides limited accuracy for cases when the non-linear part has significant length in comparison to the linear stage.
In comparison to correlation-based approaches (e.g. MAGIA), the NetGenerator tool applies a dynamical model, which is based on linear ordinary differential equations (ODE).
Benefits arising in comparison to single-technique approaches are discussed.
In particular, these types of networks need to be validated in comparison to better-understood approaches.
Generally, cyclic antibiotics display an increased activity and stability in comparison to their linear analogs.
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