Democratizing knowledge representation with BioCypher
Résumé
Standardising the representation of biomedical knowledge among all
researchers is an insurmountable task, hindering the effectiveness of many
computational methods. To facilitate harmonisation and interoperability despite
this fundamental challenge, we propose to standardise the framework of
knowledge graph creation instead. We implement this standardisation in
BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to
transparently build biomedical knowledge graphs while preserving provenances of
the source data. Mapping the knowledge onto biomedical ontologies helps to
balance the needs for harmonisation, human and machine readability, and ease of
use and accessibility to non-specialist researchers. We demonstrate the usefulness
of the framework on a variety of use cases, from maintenance of task-specific
knowledge stores, to interoperability between biomedical domains, to on-demand
building of task-specific knowledge graphs for federated learning. BioCypher
(https://biocypher.org) thus facilitates automating knowledge-based biomedical
research, and we encourage the community to further develop and use it.
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