When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI.
在临床实践中分析癌症样本基因组时，除了单核苷酸变异（SNVs）外，还发现了许多结构变异（SVs）。为识别驱动变异，必须筛选出主要候选变异。当涉及融合基因时，选择尤为困难，因此基于人工智能的高精度预测至关重要。此外，我们也希望明确这种预测如何能提供更可靠的诊断。基于我们先前开发的用于预测SNV致病性的可解释人工智能（XAI）技术，我们开发了一种适用于融合基因结构变异的可解释人工智能系统。为处理融合基因变异，我们在先前的结构变异知识图谱中补充了新数据，并改进了算法。其预测准确度与现有工具相当。更重要的是，我们的可解释人工智能系统能够解释这些预测的依据。我们通过若干变异实例证明，这些解释在致病基础机制层面具有合理性。这些成果可视为迈向基因组医学未来的一步，未来在人工智能支持下有望实现高效而精准的临床决策。
肿瘤(癌症)患者之家