Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs
This work addresses the need for explainable AI in biomedical domains like drug discovery, though it is incremental as it applies an existing method to new data.
The paper tackled the problem of making AI recommendations in biology and medicine more accurate and transparent by applying a neurosymbolic approach of multi-hop reasoning on knowledge graphs to biomedical datasets. It found that this approach outperformed the best baselines by 21.7% on average while generating biologically relevant explanations.
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.