Fig. 1

Overview of the VB-MK-LMF workflow. A priori information (left) are combined with DTI data through a Bayesian model (middle). Learning is carried out using a Variational Bayesian method which approximates the latent factors and optimal kernel weights. The model provides quantitative predictions of interaction probabilities and estimates of drug promiscuity (right). Finally, VB-MK-LMF supports the visualization and exploration of the unified “pharmacological” space. Gray indicates functionalities which may also be utilized in the VB-MK-LMF model but not explored in this paper