Sentence examples for multiple network models from inspiring English sources

The new methods can combine results from multiple network models, assess the effects of predictors at network or higher levels and account for both within- and cross-network correlations of the parameters in the network models.

Because the content of the CBN database is already dense and is intended to grow further, and because the content may be partially redundant (i.e. single entities and/or relationships may be present in multiple network models and across versions), it is of utmost importance for usability that the database has efficient search capability.

Ideally, the impact of environmental stress on all major cellular processes, e.g., proliferation, inflammatory processes, and apoptosis, can be evaluated by integrating multiple biological network models and systems biology data sets, using appropriate computational approaches.

Therefore, with the help of the proposed DNPSM, the existing challenge of accumulated interference to the same primary receiver in a multiple nodes network model can be successfully solved.

In some investigations, the spectrum access of the SU in a multiple SU network model follows the exclusively spectrum access (ESA) model, in which only one CR communication pair is allowed each time, in order to avoid accumulated interference from the secondary transmission to the primary receiver on the same channel [13][14].

By considering the cross-talking of multiple pathways, network modeling allows a more comprehensive analysis of a complex system than the pathway-centric approach.

Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs.

Due to training with a relatively small meteorological data set, multiple feed-forward neural network models are proposed to avoid the over fitting problem.

So, the network models with multiple sources and multiple sinks are worthwhile to address.  .

We apply this multiple g2g BFC framework on network models with Rocketfuel topologies.

It is shown that the regularized PFLRM improves the forecast accuracy of power output over the traditional multiple linear regression and neural network models.