Sentence examples for most modeling approaches from inspiring English sources

Nonetheless, other algorithms can easily be used within KNIME, either through its customized nodes or by linking KNIME to R modules where most modeling approaches have been implemented.

As with most modeling approaches, additional validation is needed in a variety of datasets before a gold standard is likely to be declared.

Most modeling approaches that we have discussed so far aim at capturing the (time-dependent) firing rate of a neuron under continuous, stationary stimulus conditions.

Bayesian networks [ 4- 6] are one of the most popular modeling approaches for network construction using gene expression data [ 7- 17].

Like most codon modeling approaches, PhyloCSF assumes statistical independence of each codon site from all the others in the alignment (conditional on the model parameters).

Afterwards, one of the newest and most powerful modeling approach, namely Wilcoxon generalized radial basis function network (WGRBFN) was employed to predict the compressibility factor of natural gases.

The most popular modeling approach is based on the "Modal Analysis" concept, leading to an interpretation in terms of visualized Eigenmodes.

HMMs are a central method in all of these approaches not because they are the simplest, most efficient, modeling approach that is obtained when one combines a Bayesian statistical foundation for stochastic sequential analysis with the efficient dynamic programming table constructions possible on a computer.

The most common modeling approach utilizes systems of either Ordinary or Partial Differential Equations (ODE and PDE, respectively) that directly describe the evolution of global quantities or populations over time [8].

In this section we first review the most common modeling approach for biochemical reaction networks, resulting in nonlinear ordinary differential equation systems.

The purpose of this study is to identify both the most suitable modeling approach and the best-performing optimization algorithm to calibrate the parameters contained in metabolic network models.