Democratizing knowledge representation with BioCypher

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).

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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

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

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

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

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

Patrick Aloy

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

Jan Baumbach, Michael Hartung, Andreas Maier & Niklas Probul

Earlham Institute, Norwich, UK

Balazs Bohar & Tamas Korcsmaros

Biological Research Centre, Szeged, Hungary

Balazs Bohar

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

Vincent J. Carey

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

Pornpimol Charoentong

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

Pornpimol Charoentong

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

Katharina Danhauser & Christoph Klein

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

Tunca Doğan, Bünyamin Sen & Erva Ulusoy

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

Tunca Doğan, Bünyamin Sen & Erva Ulusoy

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

Johann Dreo & Benno Schwikowski

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

Johann Dreo

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

Ian Dunham & David Ochoa

Open Targets, Wellcome Genome Campus, Hinxton, UK

Ian Dunham & David Ochoa

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

Benjamin M. Gyori & Charles Tapley Hoyt

Imperial College London, London, UK

Tamas Korcsmaros

Quadram Institute Bioscience, Norwich, UK

Tamas Korcsmaros

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

Matthias Mann & Maximilian T. Strauss

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

Matthias Mann

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

Ferdinand Popp

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

Martin Preusse

Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany

Dagmar Waltemath & Judith A. H. Wodke

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

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

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Published: 19 June 2023

DOI: https://doi.org/10.1038/s41587-023-01848-y

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