This module detection approach has been successfully used in several studies [ 6, 7, 11, 18, 22, 30].
Our module detection approach has led to biologically meaningful modules in several applications [ 9, 10, 12, 20, 28- 30] but we make no claim that it is optimal.
A module found in the reference data set will be deemed non-reproducible in the test data set if no matching module can be identified by the module detection approach in the test data set.
This paper summarizes the current literature on graph kernel and graph alignment methods for graph comparison, as well as a variety of module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods.
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.
First, the two-stage approach for module detection, iMod, is applied to the whole network to obtain a partition into several modules.
A comprehensive comparison of our approach to other module detection methodologies was performed and is discussed below to illustrate significant improvements over previous approaches.
Another module, "Order Sequence Detection," approaches the problem by examining the contextual relationships of a sequence of trading orders using an extended hidden Markov model, which identifies whether sequential changes from the extracted features are manipulative activities (or not).
Even when modules are defined using a module detection procedure, cross-tabulation based approaches face potential pitfalls.
Our module detection method followed the standard WGCNA approach that has been successfully used in multiple applications [ 8, 25, 26, 29, 35].
After network creation and module detection, the GMAT of detected modules with other (correlated) phenotypes can be calculated.
