Sentence examples for module detection algorithms from inspiring English sources

We evaluated a total of 42 module detection algorithms covering all 5 approaches (clustering, decomposition, biclustering, direct NI, and iterative NI) using the described methodology (Table 1 and Supplementary Note 2).

Network clustering algorithms, which are also referred to as community or module detection algorithms, operate on a similar basis.

More generally, a network in which each node is a protein and each edge is a pairwise match can be constructed and then analyzed with clique or module detection algorithms to increase the amount of information involved in a prediction.

A variety of module detection algorithms exist in the literature.

Most module detection algorithms are based on either network connectivity or the density of subgraphs.

Advances in module detection algorithms have elevated microarray analysis to much higher levels than simple identification of differentially expressed genes and Gene Ontology (GO) enrichment analysis.

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

We then used our module detection algorithm to identify modules in the simulated networks.

We then applied the module detection algorithm to these randomized networks to estimate the statistical significance of the Q value for each combination of m and n.

The module detection algorithm is expected to be able to identify the prescribed modules correctly when p is high, while no module can be identified by any algorithm when p is too low.

Since the module detection algorithm described below does not take into account multiple lines of evidence or self-directed edges, all redundant edges were collapsed into single edges, and all self-directed edges were pruned from the network.