Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs.
Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs.
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为了治疗由基因突变引起的疾病，如基因和癌细胞的突变，基因组医学正在推广，以使用全面的遗传分析（下一代测序，或NGS）来识别个体患者的致病变异，以进行诊断和治疗。然而，NGS输出的大量变异数据的临床解释是一项耗时的工作，已成为基因组医学推广的瓶颈。虽然支持这一任务的人工智能开发已经在各个领域进行，但尚未实现同时具有高估计精度和可解释性。因此，我们提出了一种具有高估计精度和解释能力的AI，这将消除基因组医学中的瓶颈。背景：为了治疗由遗传变异引起的疾病，有必要识别患者的致病变异。然而，由于存在大量致病变异，因此需要AI的应用。我们提出人工智能来解决这个问题，并报告其在识别致病变异方面的应用结果。研究方法：为了帮助医生识别致病变异，我们提出了一种可解释的AI（XAI），它使用知识图将高估计准确性与可解释性相结合。我们整合了基因组医学的数据库，并构建了一个用于实现XAI的大型知识图。结果：我们将XAI与随机森林和决策树进行了比较。结论：我们提出了一个使用知识图进行解释的XAI。该方法具有较高的估计性能和可解释性。这将有助于促进基因组医学。
To treat diseases caused by genetic mutations, such as mutations in genes and cancer cells, genomic medicine is being promoted to identify disease-causing variants in individual patients using comprehensive genetic analysis (next-generation sequencing, or NGS) for diagnosis and treatment. However, clinical interpretation of the large amount of variant data output by NGS is a time-consuming task and has become a bottleneck in the promotion of genomic medicine. Although AI development to support this task has been conducted in various fields, none has yet been realized that has both high estimation accuracy and explainability at the same time. Therefore, we propose an AI with high estimation accuracy and explanatory power, which will eliminate the bottlenecks in genomic medicine. 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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