Biomedical research is generating data at an unprecedented scale, and the ability to connect this information meaningfully has become essential for accelerating discovery. Knowledge graphs offer a powerful solution by integrating highly heterogeneous datasets into a single, interpretable network. By uncovering relationships across genes, pathways, diseases, drugs, biomarkers, adverse events, and clinical evidence, they enable intuitive exploration, explainable insights, and scalable analytics that directly support target discovery, mechanism of action studies, safety assessment, and translational research.
In this webinar, we will introduce a biomedical knowledge graph, a high quality, curated resource containing more than 2.4 million nodes and over 10 million edges, integrating trusted Clarivate content from CDDI, MetaBase and OFF X. The graph is built to combine proprietary, public, and client generated data into a unified framework, capturing curated relationships across biology, chemistry, and clinical evidence. This makes it a strong foundation not only for hypothesis generation and exploratory analysis, but also for downstream machine learning, AI driven analytics, and graph based applications such as retrieval augmented generation.
The webinar will answer the following questions:
- What are the key advantages of using knowledge graphs for biomedical research?
- What differentiates Clarivate’s biomedical knowledge graph from other approaches?
- How can AI and natural language querying be applied to explore complex biomedical data?
