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Mixed modelling can efficiently handle data with missing and unbalanced time-points.
Nevertheless, Boolean modelling can efficiently capture the required dynamics of a GRN and has been successfully applied in the past to model various biological phenomena, such as cellular differentiation and embryo development (Davidich and Bornholdt, 2008; Davidson et al., 2002; Li et al., 2004; Smith et al., 2007).
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If the model can efficiently represent the relevant features of the data, the modeling step dramatically reduces the number of columns in the data matrix (data reduction) without loss of relevant information or generation of artifacts.
Our model can efficiently and effectively handle multiple ordinal classes.
Population dynamics models can efficiently simulate behavior and dynamics of aquatic organisms [4 6].
Experimental results show that the proposed model can efficiently segment images with intensity inhomogeneity and Poisson noise.
The variable-index model can efficiently quantify these transitions, with the scale index varying linearly in time or space.
The model can efficiently capture flow evolution and vortex generation for random geometry and variable boundary conditions.
The proposed model can efficiently predict teamwork performance to maintain appropriate mental workload as well as ensure system safety.
The finite element models can efficiently predict the plastic damage in the coped steel beam used in beam-beam connections.
The resulting Pearson correlation coefficients values above 0.96, summarized in Table 5, demonstrate that the application-orientated models can efficiently replace the individual models.
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