从文本到翻译:使用语言模型来优先考虑临床审查的变种
Weijiang Li1, Xiaomin Li2, Ethan Lavallee3
1Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, United States.
medRxiv : the preprint server for health sciences
|January 13, 2025
概括
一个新的AI模型,ClinVar-BERT,可以分析遗传变异摘要以确定临床证据. 这种工具有助于重新评估不确定的遗传变异,推进个性化医疗.
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.
- 计算生物学 计算生物学
背景情况:
- 大多数罕见的遗传变异的特征不清楚,阻碍了个性化医疗.
- 临床实验室使用指导方针对变体进行分类,但许多仍然是不确定的意义 (VUS) 的变体.
研究的目的:
- 开发一种人工智能模型,自动从遗传变异摘要中识别临床证据.
- 为了改善临床实用性VUS的分类.
主要方法:
- 从ClinVar. 处理了超过240万个变体摘要.
- 开发了ClinVar-BERT,一个微调的BioBERT模型,用于句子级别的证据分类.
- 对BRCA1,TP53和PTEN等基因的功能查数据进行验证的ClinVar-BERT.
主要成果:
- ClinVar-BERT有效地识别了变体总结中的临床证据.
- 该模型显著分离了关键基因 (BRCA1,TP53,PTEN) 的功能影响估计.
- 在对功能查数据进行VUS分类时,获得了0.927的AUROC.
结论:
- ClinVar-BERT可以从诊断报告中辨别证据,帮助对变种进行重新评估.
- 这种人工智能工具可以为诊断实验室和专家策划小组优先考虑变体.
- 提高了基因组测序对罕见遗传变异的临床实用性.
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