通过深度学习模型从早期妊娠阶段的无细胞DNA中识别妊娠糖尿病
Yipeng Wang1, Pei Sun2, Zicheng Zhao3,4
1Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, P. R. China.
Briefings in bioinformatics
|January 3, 2024
概括
通过无细胞DNA (cfDNA) 分析和机器学习,可以对妊娠糖尿病 (GDM) 进行早期查. 这种新的方法可以在怀孕早期检测GDM生物标志物,改善孕产妇和胎儿的健康结果.
科学领域:
- 基因组学和生物信息学
- 孕产妇和胎儿医学 孕产妇和胎儿医学
- 计算生物学 计算生物学
背景情况:
- 孕期糖尿病 (GDM) 对母亲和胎儿构成重大风险,全球发病率不断增加.
- 目前的GDM诊断发生在妊娠晚期 (24周后),缺少早期并发症.
- 在无细胞DNA (cfDNA) 中的拷贝数变异 (CNV) 是GDM的潜在早期生物标志物.
研究的目的:
- 开发和验证用于早期GDM查的机器学习模型,使用母体血cfDNA测序数据.
- 为了识别与GDM相关的早期妊娠CNV生物标志物.
- 评估基于cfDNA的GDM查可行性,以便及时进行干预.
主要方法:
- 一个机器学习模型,特别是一个带有注意力的卷积神经网络,被开发用于二进制分类.
- 分析了来自中国北部5085名孕妇 (1942年患有GDM) 的低覆盖率cfDNA测序数据 (~0.2×).
- 使用非重叠的滑动窗口方法来选CNV覆盖范围.
主要成果:
- 该模型实现了高性能:准确率为88.14%,精度为84.07%,回忆率为93.04%,F1得分为88.33%,AUC为96.49%.
- 确定了2190个与GDM相关的基因,包括DEFA1,DEFA3和DEFB1.
- 丰富的基因本体学和KEGG通路分析将已识别的基因与糖尿病相关的通路联系起来.
结论:
- 对cfDNA测序数据的机器学习分析可以在怀孕期间早期进行GDM查.
- 这种方法对早期GDM诊断充满希望,促进及时干预和改善妊娠结果.
- 母亲cfDNA中的CNV是早期GDM检测的可行的生物标志物.
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