Multi-study inference of regulatory networks for more accurate models of gene regulation
Fig 3
Multitask learning improves accuracy of inferred networks.
(A) Relationship between TF activity and target expression in B. subtilis 1 (blue) and in B. subtilis 2 (orange), and corresponding STL and MTL inferred confidence scores for an example of an interaction in the B. subtilis gold-standard, sigB to ydfP. (B) as shown in (A), but for an interaction in the S. cerevisiae gold-standard, Rap1 to Rpl12a. (C) Precision-recall curves assessing accuracy of network models inferred for individual B. subtilis datasets against a leave-out set of interactions. Barplot show mean area under precision-recall curve (AUPR) for each method and dataset. Error bars show the standard deviation across 10 splits of the gold-standard into prior and evaluation set. (D) Precision-recall curves assessing accuracy of network models inferred for individual S. cerevisiae networks, with the difference that the prior is from an independent source (no splits or replicates).