Sentence examples for recovering network from inspiring English sources

We have presented a system biology approach to study the impact of environmental inputs on recovering network structures.

In Section 3.1, we gauge the performance of CSI/hCSI in recovering network structures when data was generated from identical networks albeit with perturbed dynamics, as might be encountered when dealing with identical conditions in different individuals, or when observing identical processes over different time scales.

Disasters are characterized by the occurrence of multiple failures in the physical network, as well as the possibility of correlated cascading failures during attempts to recover network resources.

In different specifications we consider the cases of one vs. multiple price zones (market splitting) and analyze different approaches to recover network cost in particular lump sum, generation capacity based, and energy based fees.

In the case of lesions, we found that the damage by the lesion could be "frozen" into the network, even though the total number of neurons is kept the same in the recovery phase as before the lesion; the recovered network might be operable, but nevertheless impaired.

From networks degraded by considerable noise, Neighborhood Correlation recovers network clique structure typical of the structure of gene families evolved through vertical descent.

They have assessed the performance of their methodology in simulations studies, showing that it can recover network edges and infer their causal direction correctly at a high rate.

Since the true network structure was known, the ability of hCSI to recover network topologies could be evaluated using the the receiver operating characteristic curve (ROC) and the precision-recall (PR) curve as used in Penfold and Wild (2011).

Different amounts of lesions lead to recovered networks of different sizes; in all cases, the chains were shortened (Figure 10B).

Comparisons between the relationships used to define the SynTReN data generation and the recovered networks have been numeric, without apparent consideration of the type of interaction.

As seen from the examples applied on random artificial networks, such data should recover networks with high fidelity because it is likely to contain enough information to recover the network.