PDF(Pubmed)
Abstract：
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.
摘要：
在临床实践中分析癌症样本基因组时，许多结构变体(SV)，除了单核苷酸变体（SNV），已被确认。要识别驱动程序变体，必须缩小主要候选人的范围。当涉及融合基因时，选择特别困难，来自AI的高度准确的预测非常重要。此外，我们还希望确定该预测如何做出更可靠的诊断.这里,我们开发了一种可解释的AI（XAI），适用于具有基因融合的SV，基于XAI技术，我们以前开发的SNV致病性预测。为了应对基因融合变异，我们在以前的SV知识图中添加了新数据，并改进了算法。其预测精度与现有工具一样高。此外，我们的XAI可以解释这些预测的原因。我们使用了一些变体示例来证明原因在致病基本机制方面是合理的。这些结果可以被视为朝着基因组医学的未来迈出的有希望的一步，在人工智能的支持下，可以做出有效和正确的决策。
