应用数据科学方法与人工智能变体重新解释,以利用临床数据绘制和估计遗传疾病流行率
Suellen Jackson1, Rebecca Freeman1,2, Adriana Noronha1
1Valley Children's Hospital, Madera, California, USA.
American journal of medical genetics. Part A
|January 3, 2024
概括
数据科学和人工智能 (AI) 重新解释了临床遗传变异,确定了可操作的见解和门德尔障碍. 这种方法使得可用于针对性医疗干预的变异流行率估计和地理映射成为可能.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 医疗信息学 医疗信息学
背景情况:
- 临床遗传数据通常是无结构的和未充分利用的.
- 数据科学提供了分析这些复杂数据的方法.
- 人工智能 (AI) 可以帮助解释遗传变异.
研究的目的:
- 应用人工智能和数据科学来重新解释临床遗传变异.
- 将变体与患者的人口统计和地理数据联系起来.
- 为了确定可操作的遗传发现,并估计疾病的患病率.
主要方法:
- 利用弗兰克林©人工智能在5年内对遗传变异进行重新解释.
- 综合变体数据与电子医疗记录使用PowerBI©.
- 用邮政编码映射变体,以识别地理热点.
主要成果:
- 确定了3065种变异,其中98%与患者匹配.
- 人工智能重新解释改变了24%的变体的解释,产生了156个可操作的重新解释.
- 确定了739种孟德尔遗传障碍,包括诸如泽尔韦格谱系障碍,巴德特-比德尔综合征和雷特综合征等特定疾病.
结论:
- 开发了一个数据库和探索性数据分析 (EDA) 方法论,用于系统的变异重新解释.
- 能够估计变种流行率,并识别可用新疗法治疗的疾病.
- 对致病性遗传变异进行丰富的局部地理区域,为公共卫生战略提供信息.
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