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Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs
Shuya Abe1, Shinichiro Tago1, Kazuaki Yokoyama2
1Artificial Intelligence Laboratory, Fujitsu Research, Fujitsu Ltd., Kawasaki 211-8588, Japan.
Background:
To treat diseases caused by genetic variants, it is necessary to identify disease-causing variants in patients. However, since there are a large number of disease-causing variants, the application of AI is required. We propose AI to solve this problem and report the results of its application in identifying disease-causing variants.
Methods:
To assist physicians in their task of identifying disease-causing variants, we propose an explainable AI (XAI) that combines high estimation accuracy with explainability using a knowledge graph. We integrated databases for genomic medicine and constructed a large knowledge graph that was used to achieve the XAI.
Results:
We compared our XAI with random forests and decision trees.
Conclusion:
We propose an XAI that uses knowledge graphs for explanation. The proposed method achieves high estimation performance and explainability. This will support the promotion of genomic medicine.
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