Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing

• URL: http://arxiv.org/abs/2007.05292v1
• Date: Fri, 10 Jul 2020 10:32:08 GMT
• Title: Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
• Authors: Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Martin Ringsquandl, Volker Tresp
• Abstract summary: We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We apply our method to the biomedical knowledge graph Hetionet and show that our approach outperforms several baseline methods.
• Score: 23.783111050856245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link prediction problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approach outperforms several baseline methods.

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