Hetnet连接搜索提供了关于生物医学实体是如何相关的快速见解
Daniel S Himmelstein1,2, Michael Zietz1,3, Vincent Rubinetti1,4
1Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
GigaScience
|July 28, 2023
概括
我们开发了一种新的网络连接搜索,以发现生物医学关系,而不需要预定义的训练数据. 这种方法在大型知识图中确定了重要的路径,使得对基因,疾病和药物联系的新见解成为可能.
科学领域:
- 生物医学信息学 生物医学信息学
- 网络科学 网络科学
- 计算生物学 计算生物学
背景情况:
- 异质网络 (hetnets) 使用多个节点和关系类型编码复杂的生物医学知识.
- 黑网,一个特定的黑网,整合了11个节点类型 (基因,疾病,药物等). 和超过200万的关系.
- 现有的药物重用监督方法需要已知的关系,限制未经探索的节点对的发现.
研究的目的:
- 开发一种方法来识别任何两个节点之间的有意义的连接在一个hetnet不需要监督的黄金标准.
- 为了使在大型生物医学知识图表中探索新型关系.
主要方法:
- 引入了"hetnet连接搜索",一种算法,可以找到节点之间的重要路径.
- 该算法根据节点级别识别出比预期的更频繁的路径类型.
- 针对大型知识图的高效预计算,实施了优化.
主要成果:
- 网络连接性搜索成功地提出了任意节点之间的重要路径.
- 该方法不依赖于预先定义的一组已知的关系.
- 节点连接的重要实例在规模上进行了预计算.
结论:
- 开发的方法为探索生物医学知识图提供了一个强大的工具.
- 一个在线接口 (https://het.io/search) 和一个开源的Python包 (hetmatpy) 可供公众使用.
- 这种方法有助于发现新的生物学和药理学关系.
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