Bayesian Matrix Factorization with Non-Random Missing Data using Informative Gaussian Process Priors and Soft Evidences

We propose an extended Bayesian matrix factorization method, which can incorporate multiple sources of side information, combine multiple a priori estimates for the missing data and integrates a flexible missing not at random submodel. The model is formalized as probabilistic graphical model and a corresponding Gibbs sampling scheme is derived to perform unrestricted inference. We discuss the application of the method for completing drug–target interaction matrices, also discussing specialties in this domain. Using real-world drug–target interaction data, the performance of the method is compared against both a general Bayesian matrix factorization method and a specific one developed for drug–target interaction prediction. Results demonstrate the advantages of the extended model.