Democratising Knowledge Graphs
Building a biomedical knowledge graph often takes months or even years.
What if you could do it in just weeks or days?
We created BioCypher to revolutionise the process—making it easier than ever while maintaining flexibility and transparency.
At its core, BioCypher is designed around the principle of threefold modularity:
- Modular data sources – Seamlessly integrate diverse biomedical datasets.
- Modular ontology structures – Define flexible, structured knowledge representations.
- Modular output formats – Adapt results to various applications and tools.
This modular approach maximises flexibility and reusability, empowering the biomedical community to accelerate research while streamlining efforts.
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BioCypher is the simplest way to create an AI-enabled knowledge graph for biomedical (or other) tasks. Check our BioChatter documentation for more information.
We have also recently published a perspective on connecting knowledge and machine learning to enable causal reasoning in biomedicine, with a particular focus on the currently emerging "foundation models." You can read it here.
New to Knowledge Graphs?
If you’re new to knowledge graphs and want to familiarise with the concepts that drive BioCypher, we recommend to check out the graphical abstract below and read our paper (self-archived version here on Zenodo, online version at this https link)!