Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

• URL: http://arxiv.org/abs/2111.10625v1
• Date: Sat, 20 Nov 2021 16:41:34 GMT
• Title: Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs
• Authors: Gavin Edwards, Sebastian Nilsson, Benedek Rozemberczki, Eliseo Papa
• Abstract summary: A neurosymbolic approach of multi-hop reasoning on knowledge graphs has been shown to produce transparent explanations. In this paper, the approach is explored for drug discovery to draw solid conclusions on its applicability. The approach is found to outperform the best baselines by 21.7% on average whilst producing novel, biologically relevant explanations.
• Score: 2.007262412327553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent. In other domains, a neurosymbolic approach of multi-hop reasoning on knowledge graphs has been shown to produce transparent explanations. However, there is a lack of research applying it to complex biomedical datasets and problems. In this paper, the approach is explored for drug discovery to draw solid conclusions on its applicability. For the first time, we systematically apply it to multiple biomedical datasets and recommendation tasks with fair benchmark comparisons. The approach is found to outperform the best baselines by 21.7% on average whilst producing novel, biologically relevant explanations.

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