Sentence examples for models gene vertex from inspiring English sources

In GR and LVD models, gene vertex degrees are ignored and occurrence probabilities for genes with large vertex degree are very small, which is compensated by larger occurrence probabilities for genes with small vertex degree.

Since this model considers gene vertex degrees, we call it the gene vertex degree (GVD) model.

In particular, it is not true for the GR and the LVD models, which do not consider gene vertex degrees.

In the GVD model, the deviations are a function of gene vertex degree and in the LVD model the deviations are dependent on list vertex degrees.

The dependency of the expected number of co-occurrences on the gene vertex degree is less obvious and depends strongly on the null-model.

In the permutation model, the sum of occurrence probabilities of a gene over all lists equals the gene vertex degree (see below) and thus the sum of all matrix elements is the number of edges.

The expected number of co-occurrences was calculated for all genes in all lists using all null-models and the sum of expected co-occurrences per gene is shown as a scatter plot with the gene vertex degree on the x-axis and the expected number of co-occurrences on the y-axis.

The occurrence probability of a gene vertex would be given by (gene vertex degree)/ lists.

Fig. 2D and E show the average occurrence probability of genes with the same gene vertex degree as a function of the gene vertex degree.

The sum of these probabilities over all lists is equal to the gene vertex degree and the sum of all gene vertex degrees is equal to the total number of edges.

The topological features of each gene (vertex) were quantified with four centrality measures, i.e. in-degree (the number of incoming edges to each vertex), out-degree (the number of outgoing edges from each vertex), betweenness (the percent of shortest paths that go through a vertex) and cluster coefficient (the percent of neighbours of a vertex that connect to each other) [ 23].