Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
This work addresses drug repurposing for biomedical researchers, offering an incremental improvement by integrating logical rules into existing reinforcement learning frameworks.
The authors tackled the problem of drug repurposing by formulating it as link prediction on biomedical knowledge graphs, proposing PoLo, a method combining reinforcement learning with logical rules, which outperformed state-of-the-art methods on Hetionet.
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems. The nature of biological data leads to a graph structure that differs from those typically encountered in benchmarking datasets. To understand the implications this may have on the performance of reasoning algorithms, we conduct an empirical study based on the real-world task of drug repurposing. We formulate this task as a link prediction problem where both compounds and diseases correspond to entities in a knowledge graph. To overcome apparent weaknesses of existing algorithms, we propose a new method, PoLo, that combines policy-guided walks based on reinforcement learning with logical rules. These rules are integrated into the algorithm by using a novel reward function. We apply our method to Hetionet, which integrates biomedical information from 29 prominent bioinformatics databases. Our experiments show that our approach outperforms several state-of-the-art methods for link prediction while providing interpretability.