TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing

TL;DR

This paper introduces a novel coupled tensor-matrix embedding framework for knowledge graphs, significantly improving drug repurposing accuracy for COVID-19 by leveraging tensor factorization techniques.

Contribution

It presents a new coupled tensor-matrix approach for knowledge graph embedding, enhancing drug repurposing performance for COVID-19.

Findings

Achieved 100% improvement over baseline in COVID-19 drug repurposing

Utilized tensor factorization for concise entity and relation representations

Applied to biological knowledge graphs for effective drug discovery

Abstract

Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases. KGs can simultaneously model many different types of subject-predicate-object and higher-order relations. As such, they offer a flexible modeling framework that has been applied to many areas, including biology and pharmacology -- most recently, in the fight against COVID-19. The flexibility of KG modeling is both a blessing and a challenge from the learning point of view. In this paper we propose a novel coupled tensor-matrix framework for KG embedding. We leverage tensor factorization tools to learn concise representations of entities and relations in knowledge bases and employ these representations to perform drug repurposing for COVID-19. Our proposed framework is principled, elegant, and achieves 100% improvement over the best baseline in the COVID-19 drug repurposing task using a recently developed biological KG.