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ClearSky's approach is very similar, delivering data from the location where it makes the most sense for the given type of data.
However, the nodes forwarding a given type of data will be primarily selected from the set of nodes running the same application to which the data is associated.
Many papers have been written to show co-relations among various centrality measures and to date, research has been carried out to answers these questions: which centrality measure is best for obtaining the top-K influential nodes in a given network, which centrality measure is the best fit for a given type of data etc.
A model-based variance estimation approach was introduced to identify genes with the lowest variance in a given type of data set and therefore best suited for normalization [30].
The tunning parameter λ is chosen according to simulation for the given type of data.
Although available methods for automatic ontology generation produce ontologies from a given type of data, such as gene networks [ 8], textual data [ 9], dictionary [ 10], and schemata [ 11, 12], they do not contribute to the integration of different types of ontologies which will bring innovative results on annotation/knowledge reuse and association studies.
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For a given type of functional association data we combined edges from the two databases by taking the union of interactions from BioGRID and STRING and represented each gene by a vector of variables, where component i indicates if the corresponding protein interacts with protein i in the combined network.
Each column summarizes the data for a given type of locomotion (Forward, Dwelling, Backward, and Quiescence) for all genotypes.
Secondly, given the type of data collection, information bias might have affected results in 2 different ways: a) we might have overestimated the number of infections as the consequence of misdiagnosis of colonizations; b) as we conducted a microbiological surveillance we considered only a fraction of all infections which could have actually occurred over the period.
Given the type of data available for this paper, we chose a quasi-experimental uninterrupted time series analysis design with a nonequivalent comparison group [ 12].
Given the type of data available for this paper, we chose to model the number of social mobilization activities (as covariates) against several vaccination outcomes---to identify which activities appear predictive of improved outcomes.
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