Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs
修也 阿部, Shin-ichiro Tago, Kazuaki Yokoyama, Miho Ogawa, Tomomi Takei, Seiya Imoto, Masaru Fuji · Cancers · 2023
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.