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Democratizing knowledge representation with BioCypher

Nature Biotechnology volume 41, pages 1056–1059 (2023) Cite this article

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Biomedical data are amassed at an ever-increasing rate, and machine learning tools that use prior knowledge in combination with biomedical big data are gaining much traction1,2. Knowledge graphs (KGs) are rapidly becoming the dominant form of knowledge representation. KGs are data structures that represent knowledge as a graph to facilitate navigation and analysis of complex information, often by leveraging semantic information. Their versatility has made them popular in areas such as data storage, reasoning, and explainable artificial intelligence3. However, for many research groups, building their own biomedical KG is prohibitively expensive. This motivated us to build the BioCypher framework to support users in creating KGs (https://biocypher.org).

The ability to build a task-specific KG is important, since directly standardizing the representation of biomedical knowledge is not appropriate for the diverse research tasks in the community. While human researchers can contextualize and abstract concepts easily, the same does not apply to algorithms. For example, drug discovery tasks (viewing genes as functional ancestors of protein targets) require a different KG structure and content from the implementation of a molecular tumor board (genes as clinical markers), which is different still from research into cell type-contextualized gene regulatory network inference (genes as targets of regulatory mechanisms). Even for similar tasks, the KG structure or subtle decisions about included resources lead to different results for many modern analytic methods2. In addition, decisions about how to represent knowledge at each primary resource pose problems in their integration — for instance, via the use of different identifier namespaces, levels of granularity or licenses4,5.

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Fig. 1: The BioCypher framework.
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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement No 965193 (DECIDER) and 116030 (TransQST)), the German Federal Ministry of Education and Research (BMBF, Computational Life Sciences grant No 031L0181B and MSCoreSys research initiative research core SMART-CARE 031L0212A), the US Defense Advanced Research Projects Agency (DARPA) Young Faculty Award (W911NF-20-1-0255), and the Medical Informatics Initiative Germany, MIRACUM consortium (FKZ: 01ZZ2019). We thank Henning Hermjakob, Benjamin Haibe-Kains, Pablo Rodriguez-Mier, Daniel Dimitrov and Olga Ivanova for feedback on the manuscript and Ben Hitz and Pedro Assis for feedback on their use of BioCypher.

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Authors and Affiliations

  1. Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany

    Sebastian Lobentanzer, Elias Farr, Denes Turei & Julio Saez-Rodriguez

  2. Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain

    Patrick Aloy, Adrià Fernandez-Torras & Elena Pareja-Lorente

  3. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Patrick Aloy

  4. Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany

    Jan Baumbach, Michael Hartung, Andreas Maier & Niklas Probul

  5. Earlham Institute, Norwich, UK

    Balazs Bohar & Tamas Korcsmaros

  6. Biological Research Centre, Szeged, Hungary

    Balazs Bohar

  7. Channing Division of Network Medicine, Mass General Brigham, Harvard Medical School, Boston, MA, USA

    Vincent J. Carey

  8. Centre for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Heidelberg, Germany

    Pornpimol Charoentong

  9. Department of Medical Oncology, National Centre for Tumour Diseases (NCT), Heidelberg University Hospital (UKHD), Heidelberg, Germany

    Pornpimol Charoentong

  10. Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital, LMU Munich, Munich, Germany

    Katharina Danhauser & Christoph Klein

  11. Biological Data Science Lab, Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Tunca Doğan, Bünyamin Sen & Erva Ulusoy

  12. Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey

    Tunca Doğan, Bünyamin Sen & Erva Ulusoy

  13. Computational Systems Biomedicine Lab, Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France

    Johann Dreo & Benno Schwikowski

  14. Bioinformatics and Biostatistics Hub, Institut Pasteur, Université Paris Cité, Paris, France

    Johann Dreo

  15. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK

    Ian Dunham & David Ochoa

  16. Open Targets, Wellcome Genome Campus, Hinxton, UK

    Ian Dunham & David Ochoa

  17. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA

    Benjamin M. Gyori & Charles Tapley Hoyt

  18. Imperial College London, London, UK

    Tamas Korcsmaros

  19. Quadram Institute Bioscience, Norwich, UK

    Tamas Korcsmaros

  20. Proteomics Program, Novo Nordisk Foundation Centre for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Matthias Mann & Maximilian T. Strauss

  21. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

    Matthias Mann

  22. Applied Tumour Immunity Clinical Cooperation Unit, National Centre for Tumour Diseases (NCT), German Cancer Research Centre (DKFZ), Heidelberg, Germany

    Ferdinand Popp

  23. German Centre for Diabetes Research (DZD), Neuherberg, Germany

    Martin Preusse

  24. Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany

    Dagmar Waltemath & Judith A. H. Wodke

Authors
  1. Sebastian Lobentanzer
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  2. Patrick Aloy
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  8. Tunca Doğan
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  13. Benjamin M. Gyori
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  14. Michael Hartung
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  16. Christoph Klein
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  17. Tamas Korcsmaros
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  18. Andreas Maier
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  19. Matthias Mann
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  21. Elena Pareja-Lorente
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  22. Ferdinand Popp
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  23. Martin Preusse
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  24. Niklas Probul
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  25. Benno Schwikowski
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  26. Bünyamin Sen
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  27. Maximilian T. Strauss
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  28. Denes Turei
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  29. Erva Ulusoy
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  30. Dagmar Waltemath
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  31. Judith A. H. Wodke
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  32. Julio Saez-Rodriguez
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Contributions

The project was conceived by S.L. and J.S.-R. The software was developed by S.L. with input from D.T. The manuscript was drafted by S.L., edited by J.S.-R. and jointly revised by all co-authors. All co-authors as members of the BioCypher Consortium contributed to the case studies in development and writing and gave feedback for software development, which was coordinated and integrated by S.L. All authors except first and last are listed alphabetically by surname.

Corresponding authors

Correspondence to Sebastian Lobentanzer or Julio Saez-Rodriguez.

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Competing interests

J.S.-R. reports funding from GSK, Pfizer and Sanofi and fees from Travere Therapeutics and Astex Pharmaceuticals.

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Peer review information

Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

Supplementary information

Supplementary Information (download PDF )

Supplementary Methods, Supplementary Notes 1–5 and Supplementary Table 1

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Lobentanzer, S., Aloy, P., Baumbach, J. et al. Democratizing knowledge representation with BioCypher. Nat Biotechnol 41, 1056–1059 (2023). https://doi.org/10.1038/s41587-023-01848-y

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