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A platform for the biomedical application of large language models

Nature Biotechnology volume 43, pages 166–169 (2025)Cite this article

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Generative artificial intelligence (AI) has advanced considerably in recent years, particularly in the domain of language. However, despite its rapid commodification, its use in biomedical research is still in its infancy1,2. The two main avenues for using large language models (LLMs) are end-user-ready platforms, which are usually provided by large corporations, and custom solutions developed by individual researchers with programming knowledge. Both use cases have significant limitations. Commercial platforms do not meet the transparency standards required for reproducible research; none are open source, and only a few provide (superficial) scientific descriptions of their algorithms3. They are also subject to privacy concerns (reuse of user data) and to considerable commercial pressures. In addition, they are not fully customizable to accommodate a specific research domain or workflow.

Individual solutions, on the other hand, are not accessible to most biomedical researchers. They require many specialized skills in addition to the researcher’s domain-specific knowledge, such as programming, data management, machine learning knowledge, technical expertise in deployment and frameworking, and management of software versions in a rapidly changing environment. This, in turn, prevents robust and reproducible results owing to the many technical challenges involved. As a result, applications of LLMs in biomedical research are still at the level of individual case studies2,4, in contrast to the imaging domain, which boasts several open-source AI frameworks and approved medical devices1.

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Fig. 1: The modular BioChatter platform architecture.
Fig. 2: Benchmarking, monitoring, and outlook.

Data availability

All data used in this study are available in the repository at https://github.com/biocypher/biochatter. In addition, the repository is DOI-indexed at Zenodo/OpenAIRE (https://doi.org/10.5281/zenodo.10777945).

Code availability

All code used in this study is available in the repository at https://github.com/biocypher/biochatter. In addition, the repository is DOI-indexed at Zenodo/OpenAIRE (https://doi.org/10.5281/zenodo.10777945).

References

  1. Perez-Lopez, R., Ghaffari Laleh, N., Mahmood, F. & Kather, J. N. Nat. Rev. Cancer 24, 427–441 (2024).

    Article  CAS  PubMed  Google Scholar 

  2. Simon, E., Swanson, K. & Zou, J. Nat. Methods 21, 1422–1429 (2024).

    Article  CAS  PubMed  Google Scholar 

  3. Liesenfeld, A. & Dingemanse, M. Rethinking open source generative AI: open-washing and the EU AI Act. In The 2024 ACM Conference on Fairness, Accountability, and Transparency, https://doi.org/10.1145/3630106.3659005 (ACM, 2024).

  4. Pividori, M. Nature https://doi.org/10.1038/d41586-024-02630-z (2024).

    Article  PubMed  Google Scholar 

  5. UNESCO. UNESCO Recommendation on Open Science. UNESCO https://doi.org/10.54677/mnmh8546 (2021).

  6. Lobentanzer, S. et al. Nat. Biotechnol. 41, 1056–1059 (2023).

    Article  CAS  PubMed  Google Scholar 

  7. Shinn, N. et al. Preprint at https://doi.org/10.48550/arxiv.2303.11366 (2023).

  8. Nezhurina, M., Cipolina-Kun, L., Cherti, M. & Jitsev, J. Preprint at https://doi.org/10.48550/arxiv.2406.02061 (2024).

  9. van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R. & Bockting, C. L. Nature 614, 224–226 (2023).

    Article  PubMed  Google Scholar 

  10. Bockting, C. L., van Dis, E. A. M., van Rooij, R., Zuidema, W. & Bollen, J. Nature 622, 693–696 (2023).

    Article  CAS  PubMed  Google Scholar 

  11. Lee, P., Goldberg, C. & Kohane, I. The AI Revolution in Medicine: GPT-4 and Beyond (Pearson, 2023).

  12. Schaefer, M. et al. Joint embedding of transcriptomes and text enables interactive single-cell RNA-seq data exploration via natural language. In ICLR 2024 Workshop on Machine Learning for Genomics Explorations https://openreview.net/forum?id=yWiZaE4k3K (2024).

  13. Lobentanzer, S., Rodriguez-Mier, P., Bauer, S. & Saez-Rodriguez, J. Mol. Syst. Biol. 20, 848–858 (2024).

  14. Chakravarty, D. et al. JCO Precis. Oncol. https://doi.org/10.1200/po.17.00011 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Camacho, C. et al. BMC Bioinformatics 10, 421 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank H. Schumacher, D. Dimitrov and P. Badia i Mompel for feedback on the original draft of the manuscript and the software. This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no 965193 for DECIDER (JSR), awards U54AG075931 and R01DK138504 (QM) from the National Institutes of Health, and the Pelotonia Institute for Immuno-Oncology. This manuscript was written using Manubot (https://github.com/manubot) and partially revised using LLMs. The entire manuscript was double-checked for correctness, and the responsibility for the final content lies with the authors only. This project is funded by the European Union under grant agreement 101057619. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. This work was also partly supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract 22.00115.

Author information

Authors and Affiliations

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

    Sebastian Lobentanzer, Aurelien Dugourd, Valeriia Dragan, Nils Krehl & Julio Saez-Rodriguez

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

    Sebastian Lobentanzer & Ellen M. McDonagh

  3. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA

    Shaohong Feng, Megan McNutt, Cankun Wang & Qin Ma

  4. Michael Sars Centre, University of Bergen, Bergen, Norway

    Noah Bruderer & Lionel Christiaen

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

    Andreas Maier, Fernando M. Delgado-Chaves & Jan Baumbach

  6. Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark

    Jan Baumbach

  7. EMBO, Heidelberg, Germany

    Hannah Sonntag, Jorge Abreu-Vicente & Thomas Lemberger

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

    Melissa Harrison, Yuyao Song & Julio Saez-Rodriguez

  9. Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium

    Adrián G. Díaz

  10. Structural Biology Brussels, Vrije Universiteit Brussels, Brussels, Belgium

    Adrián G. Díaz

  11. The Francis Crick Institute, London, UK

    Amy Strange

  12. Laboratory of Multi-Omic Integrative Bioinformatics, Center for Human Genetics, Faculty of Medicine, Katholieke Universiteit Leuven, Leuven, Belgium

    Anis Ismail

  13. Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany

    Anton Kulaga

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

    Barbara Zdrazil

  15. Laboratory of Probability, Statistics, and Modelling, Sorbonne University, Paris, France

    Bastien Chassagnol

  16. Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, Versailles, France

    Cyril Pommier

  17. Université Paris-Saclay, INRAE, URGI, Versailles, France

    Cyril Pommier

  18. Institute of Translational Cancer Research and Experimental Cancer Therapy, Technical University of Munich, Munich, Germany

    Daniele Lucarelli

  19. Department of Computational Health, Institute of Computational Biology, Helmholtz, Munich, Germany

    Daniele Lucarelli

  20. Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium

    Emma Verkinderen

  21. Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA), Rostock University Medical Center, Rostock, Germany

    Georg Fuellen

  22. Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center – University of Freiburg, Freiburg, Germany

    Jonatan Menger

  23. Core for Computational Biomedicine, Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Ludwig Geistlinger

  24. Heilbronn University of Applied Sciences, Heilbronn, Germany

    Luna Zacharias Zetsche, Marlis Engelke & Patrick Baracho

  25. GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany

    Melissa Hizli & Yasmin Nielsen-Tehranchian

  26. HEAlthy Life Extension Society (HEALES), Brussels, Belgium

    Nikolai Usanov

  27. Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany

    Sebastian Beier & Xiao-Ran Zhou

  28. Bioinformatics and Biostatistics Science Technology Platform and Software Engineering and AI Science Technology Platform, The Francis Crick Institute, London, UK

    Stefan Boeing

  29. Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland

    Taru A. Muranen

  30. Bristol Myers Squibb, Cambridge, MA, USA

    Trang T. Le

Authors
  1. Sebastian Lobentanzer
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  2. Shaohong Feng
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  3. Noah Bruderer
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  4. Andreas Maier
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  5. Cankun Wang
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  6. Jan Baumbach
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  7. Jorge Abreu-Vicente
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  8. Nils Krehl
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  9. Qin Ma
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  10. Thomas Lemberger
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  11. Julio Saez-Rodriguez
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Consortia

The BioChatter Consortium

  • Adrián G. Díaz
  • , Amy Strange
  • , Andreas Maier
  • , Anis Ismail
  • , Anton Kulaga
  • , Aurelien Dugourd
  • , Barbara Zdrazil
  • , Bastien Chassagnol
  • , Cankun Wang
  • , Cyril Pommier
  • , Daniele Lucarelli
  • , Ellen M. McDonagh
  • , Emma Verkinderen
  • , Fernando M. Delgado-Chaves
  • , Georg Fuellen
  • , Hannah Sonntag
  • , Jan Baumbach
  • , Jorge Abreu-Vicente
  • , Jonatan Menger
  • , Julio Saez-Rodriguez
  • , Lionel Christiaen
  • , Ludwig Geistlinger
  • , Luna Zacharias Zetsche
  • , Marlis Engelke
  • , Megan McNutt
  • , Melissa Harrison
  • , Melissa Hizli
  • , Nikolai Usanov
  • , Nils Krehl
  • , Noah Bruderer
  • , Patrick Baracho
  • , Qin Ma
  • , Sebastian Beier
  • , Sebastian Lobentanzer
  • , Shaohong Feng
  • , Stefan Boeing
  • , Taru A. Muranen
  • , Thomas Lemberger
  • , Trang T. Le
  • , Valeriia Dragan
  • , Xiao-Ran Zhou
  • , Yasmin Nielsen-Tehranchian
  •  & Yuyao Song

Contributions

Authors between consortium and last author are ordered alphabetically by first name. S.L. conceptualized and developed the platform, coordinated the consortium, and wrote the manuscript. S.F. implemented BioChatter functionality and developed both frontend and backend components for the BioChatter Next server. N.B. developed the API calling module with S.F. and S.L. A.M. implemented the local deployment functionality. The BioChatter consortium members contributed to the development of the platform and provided feedback on the manuscript. C.W. architected the BioChatter Next server infrastructure. J.B. provided guidance and supervision as well as hardware resources for local LLM use and contributed to performance benchmarking. J.A.-V. developed text extraction benchmarking procedures. N.K. implemented benchmarking procedures. Q.M. oversaw the development and deployment of the BioChatter Next server environment. T.L. oversaw the text extraction work and acquired funding. J.S.-R. supervised the project, revised the manuscript, and acquired funding. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sebastian Lobentanzer or Julio Saez-Rodriguez.

Ethics declarations

Competing interests

J.S.-R. reports funding from GSK, Pfizer and Sanofi and fees or honoraria from Travere Therapeutics, Stadapharm, Pfizer, Grunenthal, Owkin, Moderna and Astex Pharmaceuticals.

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Notes, including Supplementary Figs. 1–10

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Lobentanzer, S., Feng, S., Bruderer, N. et al. A platform for the biomedical application of large language models. Nat Biotechnol 43, 166–169 (2025). https://doi.org/10.1038/s41587-024-02534-3

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