Deep Matrix Completion on Graphs: Application in Drug Target Interaction Prediction

Abstract

This work proposes matrix completion via deep matrix factorization on graphs. The work is motivated by the success of two very recent studies on (shallow) matrix completion on graphs and deep matrix factorization (without graphs). We show that the proposed deep matrix factorization on graphs improves over both - shallow techniques on graphs and deep matrix factorization. Experiments are carried out on the challenging real-life problem of modeling drug-target interactions.