Drug Target Interaction Prediction with Non-random Missing Labels

Abstract

Drug-Target Interaction (DTI) prediction plays an important role in drug discovery and drug repurposing. DTI prediction is usually modeled as a binary classification problem. Unlike previous studies which label unknown DTIs as negative samples, we assume the unknown DTIs are labels that are missing not at random. For example, negative DTI labels are more likely to be missing because biomedical researchers prioritize to study DTIs that are more likely to be positive. We introduce a novel probabilistic model, Factorization with Non-random Missing Labels (FNML), for DTI prediction. FNML models the generative process for the DTI labels (i.e. the labels are positive or negative) and responses (i.e. the labels are observed or missing). In particular, the probability of observing or missing a label is associated with the sign of the label. We also conduct comprehensive experiments to validate the robust performance of the proposed models.