Ancestral reconstruction of protein interaction networks
Fig 3
A.) Performance on simulated training and test (hold-out) data when the model is trained by maximizing either the likelihood or the average precision score (APS). Fitting by APS outperforms fitting under likelihood. The APS is also used as a criterion for goodness of fit and results include all simulation parameter combinations B.) Performance as a function of the mixture weight λ1, the false negative rate, δ, the distance between the means of positive and negative interactions, and the equilibrium frequency π1, which is the expected frequency of positive interactions and is therefore proportional to class imbalance. π1 is also the expected APS value from a random guess.