Clinical Knowledge Graph

We have developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making. Here, we are uploading a dump of CKG's database that can be used to easily create a Neo4 graph database harmonizing 9 ontologies, 26 relevant biomedical databases. Experimental studies included in the publication are also included as CKG reports. To get the full database up and running follow the installation instructions specified in CKG's documentation (https://github.com/MannLabs/CKG) and GitHub repository (https://github.com/MannLabs/CKG).我们开发了临床知识图谱（Clinical Knowledge Graph，CKG），这是一款开源平台，当前包含超过1600万个节点与2.2亿条关系，用于建模表示相关实验数据、公共数据库及学术文献。该平台集成了前沿的统计与机器学习算法，可大幅加速典型蛋白质组学工作流程的分析与解读。我们依托多项生物标志物研究，阐释了CKG如何支持、丰富并加速临床决策制定。本次我们上传了CKG数据库的转储文件，可用于便捷构建整合9种本体（ontologies）与26个相关生物医学数据库的Neo4图数据库。本论文中收录的实验研究亦将以CKG报告的形式一并提供。如需完整部署并运行该数据库，请遵循CKG官方文档及GitHub仓库（https://github.com/MannLabs/CKG）中指定的安装指南进行操作。

搜集汇总

背景与挑战

背景概述

Clinical Knowledge Graph是一个开源平台，包含超过1600万个节点和2.2亿个关系，整合了9个本体和26个生物医学数据库，用于加速蛋白质组学工作流的分析和解释。该数据集支持临床决策，并提供详细的安装指南和GitHub资源。