Sentence examples for predictions of networks from inspiring English sources
By applying a procedure for cross-platform data tests we are able to present a dataset, also validated by RT-qPCR, which grants more reliable analyses at the functional genomics level and more robust predictions of networks of co-regulated microRNA target genes.
We demonstrate that this approach produces reliable and limited predictions of network architecture under constrained sample sizes, with the potential to generate more efficient network models for complex systems.
These approaches allow gene network reconstructions based on microarray time-series data, mainly in a dynamic Bayesian statistical framework [ 6, 7]; predictions of network behavior can be made based on formal approaches [ 8, 9].
Modelling not only allows predictions of network behaviours but can also determine the parameter regime where a specific type of behaviour is found, thus reducing the number of constructs that need to be made.
To experimentally validate these predictions of network analysis studies, we analyzed the expression levels of all ERBB family members by real time PCR in control and DCD shRNA expressing cells.
The contribution of the different datasets to the prediction of networks is given by dataset specific weights.
The cohesive energy density is a better tool for relative prediction of network mechanical properties.
The kinetic competition for H2O2 within bacteria is complex, which suggests that quantitative models will improve interpretation and prediction of network behavior.
Accurate characterization and prediction of network traffic is essential for network resource sizing and for real-time network management.
Node centrality is an important quantity to consider in studying complex networks as it is related to many applications ranging from the prediction of network structure to the control of dynamics on networks.
We introduce methods for analyzing the spatial propagation of cell death signals, and discuss the concepts of sensitivity analyses that enable a prediction of network responses to disturbances of single or multiple parameters.
