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.
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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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