通过整合基于规则的和机器学习算法来诊断罕见的门德尔乱,对遗传变异进行可解释的优先排序
Ho Heon Kim1, Dong-Wook Kim1, Junwoo Woo1
1Research and Development Center, 3billion, 14th floor, 416 Teheran-ro, Gangnam-gu, Seoul, 06193, Republic of Korea.
Human genomics
|March 21, 2024
概括
一个新的可解释算法,3ASC通过整合临床解释特征和ACMG/AMP指南来提高灵敏度和可解释结果,提高了罕见疾病变异优先级.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 罕见疾病遗传学 罕见疾病遗传学
背景情况:
- 准确的基因变异评估对于诊断罕见疾病至关重要.
- 现有的变体优先级工具往往缺乏灵敏度和可解释性,因为它们依赖于in-silico预测.
- 需要先进的方法,在罕见疾病研究中有效优先考虑致病变体.
研究的目的:
- 开发和验证一个可解释的算法,3ASC,以改善遗传变异优先级.
- 提高敏感性,并提供可解释的证据,以优先考虑罕见疾病的变异.
- 将临床解释特征和ACMG/AMP指导方针整合到一个新的优先级框架中.
主要方法:
- 训练有素的机器学习模型,包括随机森林分类器,使用内部患者数据.
- 使用识别致病变异的回忆率来评估变异排名表现.
- 带有28个ACMG/AMP标准和临床解释特征的注释基因变异.
主要成果:
- 随机森林模型实现了顶级1回忆率为85.6%,顶级3回忆率为94.4%.
- 在CAGI6 SickKids挑战期间,3ASC成功地在14个案例中10个案例中确定了因果基因.
- 在6例中发现了基因表达减少的证据,其中两种基因 (HDAC8,CASK) 通过转录组数据得到证实.
结论:
- 与以前的方法相比,3ASC在优先考虑遗传变异方面表现出更高的灵敏度.
- 该算法集成了各种临床解释特征,包括质量控制和遗传模式,以减少假阳性.
- 可解释的人工智能技术可以根据ACMG/AMP标准和特征贡献清晰地解释变体优先级.
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