Sentence examples for hub classification from inspiring English sources

We pursued both approaches to hub classification.

While functional cartography does not provide a definitive or "gold standard" hub classification, it is a well-studied method [21], [24], [38] that does not rely solely on the number of connections (degree) to identify hubs.

An additional limitation arises from the alternate hub classification scheme using pc-pk space, which does not assume a normal distribution as in the p-z space classification method.

As an example, Table 1 provides threshold criteria used for each method for subject 5. Since the true hub classification is not known for brain networks, functional cartography was utilized as an alternate to centrality measures.

Once modules were detected, different solutions were ranked according to a cost function and the optimal modularity (out of 10000 solutions for a range of between 2 and 6 modules) was used as the basis for hub classification.

We focused the machine-learning effort on hub classification by applying boosting trees, which is one of the best methods for classifying complex data and providing interpretable results [ 45].

The number of assigned hub and non-hub classifications is shown in Table 1. Figure 2 illustrates the subsequent steps involved in the development of the hub protein classifiers and their corresponding bioinformatics analyses.

To determine the differences between acute activation and priming, the blue and red modules were compared using two approaches: hub gene classifications and ranked gene set enrichment analysis.

The training and testing of the hub-predicting classification trees were performed on 125 GO terms as predictor variables by using the boosting tree application as implemented in STATISTICA version 8 [ 46].

The biological role of hubs allows for their classification into "party" hubs and "date" hubs [ 2].

In the case of Reggio Emilia, the municipality was mainly interested in collecting data from heterogeneous authoritative sources, integrating them into a spatial RDBMS modelled according to INSPIRE (buildings) coupled with, for the energy part, the CityGML Energy ADE, in order to create a coherent information hub regarding energy classification and energy consumption of buildings.