Bayesian Matrix Factorization with Non-Random Missing Data using Informative Gaussian Process Priors and Soft Evidences
Bence Bolgár, Péter Antal · Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) · 2016
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.