Semantic Disambiguation of Embedded Drug-Disease Associations Using Semantically Enriched Deep-Learning Approaches

Abstract

State-of-the-art approaches in the field of neural-embedding models (NEMs) enable progress in the automatic extraction and prediction of semantic relations between important entities like active substances, diseases, and genes. In particular, the prediction property is making them valuable for important research-related tasks such as hypothesis generation and drug-repositioning. A core challenge in the biomedical domain is to have interpretable semantics from NEMs that can distinguish, for instance, between the following two situations: a) drug $x$ induces disease $y$ and b) drug $x$ treats disease $y$. However, NEMs alone cannot distinguish between associations such as treats or induces. Is it possible to develop a model to learn a latent representation from the NEMs capable of such disambiguation? To what extent do we need domain knowledge to succeed in the task? In this paper, we answer both questions and show that our proposed approach not only succeeds in the disambiguation task but also advances current growing research efforts to find real predictions using a sophisticated retrospective analysis.