驱动-KG:使用异质知识图表在未研究的复杂疾病中增强变异-表型关联发现
Ananya Rajagopalan1, Tram Anh Nguyen1, Lindsay A Guare1
1Genomics and Computational Biology Graduate Program, Philadelphia, PA, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
整合多omics数据与DRIVE-KG推进了对子宫内膜异位症的理解. 这种知识图识别了新的遗传关联,改善了患者分类,为妇女健康研究提供了新的途径.
科学领域:
- 基因组学和生物信息学
- 妇女健康研究 妇女健康研究
- 计算生物学 计算生物学
背景情况:
- 子宫内膜异位症是一种普遍但未被充分研究的女性健康状况,影响10%的生育年龄女性.
- 子宫内膜异位症的有限遗传特征,目前的GWAS仅解释了11%的遗传性,需要整合性方法.
- 图形表示为协调各种生物数据提供了强大的框架.
研究的目的:
- 介绍DRIVE-KG,一个新的知识图,用于整合多omics数据来研究复杂的疾病,如子宫内膜异位症.
- 使用DRIVE-KG.开发和评估用于疾病风险推断和患者级分类的机器学习模型.
- 发现新的单核酸多态 (SNP) - 现型关联,并提高子宫内膜异位症的诊断能力.
主要方法:
- 构建了一个异质图 (DRIVE-KG) 集成多种多omics数据集.
- 训练了一个链接预测模型来识别SNP-表型关联 (子宫内膜异位症,肥胖症).
- 开发了一个图形卷积网络 (GCN) 用于患者级别的子宫内膜异位症/腺肌的分类.
主要成果:
- 确定了66个高可信度候选SNP-子宫内膜异位症关联,并为肥胖和抑郁障碍特征进行了丰富.
- 发现了对子宫内膜异位症的新型遗传信号,与已知的肥胖相关性不同.
- 通过使用GCN进行子宫内膜异位症/腺菌症分类,获得F1评分0.752,超过遗传风险评分 (0.698).
- 在GCN中,疾病严重程度和腺菌症信号的分层有意义.
结论:
- 通过DRIVE-KG将多种omics数据的异质整合有效用于未研究疾病的发现和临床预测.
- 驱动-KG促进揭示新型遗传见解子宫内膜异位症的病因.
- 开发的GCN模型显示了改善子宫内膜异位症/腺菌的诊断和分层的潜力.
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