This may result from low dependency measures of relevant interactions of genes in a more complex context being excluded from a relevance network by a global threshold.
Finally, both GANs agreed with the relevance network on some established regulatory interactions as can be seen in Figure 5.
ARACNE constructs a relevance network based on estimated mutual information between all pairs genes, but there is a twist.
A relevance network, for example, is constructed by estimating the pairwise correlations between all random variables, connecting any pair with correlation exceeding a specified cutoff value [9].
A related approach goes one step beyond a relevance network by estimating a GAN based on not only marginal but also first-order conditional independencies [11].
The only two places were the graphical lasso wins over the relevance network are the precision at 1% and 10% recall for the full data.
The relevance network contained an edge between two genes if the absolute Pearson correlation between their expression profiles is at least 0.85; all singleton nodes were deleted.
Table 4 shows that the performance depends somewhat on the threshold with the highest scores for threshold 0, that is, the relevance network (REL. in Table 4).
The simplest model based on this measure, the "relevance network", computes MI between all pairs of genes and infers the presence of a regulatory interaction when MI is larger than a given threshold [10].
This graph, often referred to as a relevance network [37], has nodes representing genes and edges representing co-expression between genes, in the sense that according to some metric the similarity of the expression patterns is above some threshold.
After a relevance network is inferred by connecting any pair of genes with mutual information greater than a certain cutoff value, some edges suspected to represent indirect interactions are eliminated using the data processing inequality principle.
