使用知识图和随机森林预测与女性生殖障碍相关的营养和环境因素
Lauren E Chan1, Elena Casiraghi2,3, Tim Putman4
1Oregon State University, College of Public Health and Human Sciences, Corvallis, OR, USA.
medRxiv : the preprint server for health sciences
|July 28, 2023
概括
这项研究使用了知识图来识别与女性生殖障碍 (FRD) 相关的潜在因素. 这些发现为未来关于环境和饮食对FRD的影响的研究提出了假设.
科学领域:
- 生殖健康 生殖健康
- 计算生物学 计算生物学
- 环境健康 环境健康
背景情况:
- 女性生殖系统疾病 (FRDs) 很普遍,并可能导致显著的症状.
- 饮食和环境因素是FRD干预的潜在目标.
- 预测FRD与各种暴露之间的关联对于产生假设至关重要.
研究的目的:
- 用知识图 (KG) 方法预测与常见FRD相关的因素.
- 确定FRD与环境/饮食暴露之间的潜在联系.
主要方法:
- 协调调查数据 (个性化环境和基因研究) 与生物医学本体学和营养素/化学数据,以构建KG.
- 使用边缘嵌入和随机森林预测分析KG,并与后勤回归进行比较.
主要成果:
- 在9,765名受访者中,确定了8535个FRD与化学品,表型和疾病之间的显著预测联系.
- 32个链接与后勤回归完全匹配,包括并发病,药物,食物和职业暴露.
结论:
- 这项调查产生了关于FRD与各种风险之间的潜在联系的假设.
- 在大型的,基于调查的数据集中,KG方法论对于生成假设非常有价值.
- 建议在未来进行前性研究,以验证影响FRD的假设变量.
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