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, Justin Reese3
1Oregon State University, College of Public Health and Human Sciences, Corvallis, OR, USA.
International journal of medical informatics
|April 21, 2024
概括
一种知识图 (KG) 方法确定了女性生殖障碍 (FRD) 与各种环境因素之间的潜在联系. 这种方法有助于为未来对FRD原因的研究提出假设.
科学领域:
- 生殖健康 生殖健康
- 生物信息学是一种生物信息学.
- 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.
研究的目的:
- 用知识图 (KG) 预测与常见的FRD相关的因素,如子宫内膜异位症,卵巢囊和子宫纤维瘤.
主要方法:
- 协调调查数据 (个性化环境和基因研究 - PEGS) 与生物医学本体学和营养/化学数据,以创建KG.
- 分析KG使用边缘嵌入和随机森林进行边缘预测.
- 将KG发现与后勤回归分析进行比较.
主要成果:
- 在9765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或暗示性联系.
- 32个链接准确匹配后勤回归结果,包括并发病,药物,食物和职业暴露.
结论:
- 该KG方法有效地预测大型数据集中的潜在关联,提供对FRD暴露链接的见解.
- 这些发现支持对未来关于FRD病因学的前性研究提出假设.
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