将罕见疾病集成到一个由本体学丰富的知识图中.
Jaleal Sanjak1,2, Jessica Binder1, Arjun Singh Yadaw1
1Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 27, 2023
概括
这项研究引入了一种新的方法,用于使用知识图和节点嵌入集群罕见疾病. 该方法成功地确定了共同的疾病特征,为药物重新定位和个性化医疗策略铺平了道路.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 识别罕见疾病的共同病因学和病理生理学方面对于药物重新用途至关重要.
- 目前的方法可能无法完全捕捉罕见疾病之间的复杂关系.
研究的目的:
- 开发和验证基于知识图的方法,用于集群罕见疾病.
- 通过识别具有共同特征的罕见疾病群体,促进药物的重新用途.
主要方法:
- 从NCATS遗传和罕见疾病信息中心提取了3242种罕见疾病的数据.
- 构建了一个与生物医学数据 (基因本体学,途径,药物向活性) 丰富的整合性知识图.
- 应用节点嵌入用于集群和验证集群使用语义相似性和基因丰富分析.
主要成果:
- 产生了37个疾病集群,每个集群平均包含87种疾病.
- 使用Orphanet罕见疾病本体学语义相似性的量化验证集群.
- 在疾病集群中确定了高度相关的丰富基因,表明了共同的生物学基础.
结论:
- 节点嵌入对于在异质知识图中集群疾病是有效的.
- 开发的方法揭示了语义上类似的疾病和相关的基因.
- 这些发现为药物重新利用提供了基础,通过列举疾病集群和药物之间的联系.
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