Sentence examples for module detection strategy from inspiring English sources

Similar to Table 1, the first and second columns list the name of the method and module detection strategy, respectively.

Figure 1 illustrates the entire module detection strategy, iMod, encompassing Stages 1 and 2 of the mathematical programming algorithm reported above.

Next, we discuss module detection strategies that include five subsections covering seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods.

The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates.

Graph comparison and module detection are two commonly used strategies for analyzing PPI networks.

Using the Newman-Girvan module detection algorithm, we detected a total of 10 modules, with an overall network modularity of 0.519.

After network creation and module detection, the GMAT of detected modules with other (correlated) phenotypes can be calculated.

Of these computational methods, graph comparison and module detection are the two most commonly used strategies.

Graph comparison and module detection are the two most commonly used strategies for analyzing PPI or other biological networks.

This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods.

Here, we aim to enhance the application of mathematical programming to community structure identification by: (i) developing an efficient methodology for module detection that is capable of handling large size networks and (ii) incorporating strategies for dealing systematically with the problem of a resolution limit in module detection through modularity optimisation approaches.