Biomedical knowledge graphs are powerful tools for organizing complex biological information and uncovering relationships that are difficult to see in traditional data formats. For part 2 of this series, we will show how researchers new to knowledge graphs can use them to support hypothesis generation, target discovery and data exploration.

We will explore:

- Knowledge graph fundamentals

- How biological entities and relationships are represented

- How to query, navigate and interpret the interconnected biomedical data within the QIAGEN Biomedical Knowledge Base

No prior experience with graph databases or graph analytics is required – just curiosity about new ways to work with biomedical knowledge.