对随着时间的推移重新分类的变体进行renovo预测的准确性
Emanuele Bonetti1, Giulia Tini1, Luca Mazzarella2
1Department of Experimental Oncology, European Institute of Oncology, IEO-IRCCS, Milan, 20139, Italy.
Journal of translational medicine
|July 31, 2024
概括
该RENOVO工具准确地预测了遗传变异的致病性,并正确地分类了82.6%的重新分类变异. 这种机器学习方法通过解决日益增长的解释差距来帮助临床基因组学.
科学领域:
- 基因组医学是基因组医学.
- 临床基因组学 临床基因组学
- 生物信息学是一种生物信息学.
背景情况:
- 解释遗传变异对于诊断遗传性疾病和癌症至关重要.
- 变体的临床验证落后于发现,造成"解释差距"和患者的不确定性.
- 随着时间的推移,随着新证据的出现,不确定的意义的变体 (VUS) 可以重新分类.
研究的目的:
- 评估基于森林的随机工具RENOVO在预测变异病原性方面的准确性.
- 评估RENOVO在四年内改变分类状态的变体上的表现.
主要方法:
- 从2020年3月到2024年3月检索了16个ClinVar数据库实例.
- 分析了变体分类的时间趋势,并确定了重新分类的变体.
- 将2020年起的RENOVO预测与2024年3月之前的实际重新分类进行了比较.
主要成果:
- 不确定意义的变异 (VUS) 是ClinVar中最常见的类别 (44.97%).
- 在2020年至2024年之间重新分类的VUS变体中,RENOVO正确分类了82.6%.
- 该工具准确地识别了在良性和致病性分类之间切换的变异.
结论:
- 目前的变体解释模型很难跟上变体发现的步伐.
- 像RENOVO这样的机器学习工具显示出高精度和潜在的帮助临床实践和基因组学研究.
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