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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
Perez-Lopez, R., Ghaffari Laleh, N., Mahmood, F. & Kather, J. N. Nat. Rev. Cancer 24, 427–441 (2024).
Simon, E., Swanson, K. & Zou, J. Nat. Methods 21, 1422–1429 (2024).
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).
Pividori, M. Nature https://doi.org/10.1038/d41586-024-02630-z (2024).
UNESCO. UNESCO Recommendation on Open Science. UNESCO https://doi.org/10.54677/mnmh8546 (2021).
Lobentanzer, S. et al. Nat. Biotechnol. 41, 1056–1059 (2023).
Shinn, N. et al. Preprint at https://doi.org/10.48550/arxiv.2303.11366 (2023).
Nezhurina, M., Cipolina-Kun, L., Cherti, M. & Jitsev, J. Preprint at https://doi.org/10.48550/arxiv.2406.02061 (2024).
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R. & Bockting, C. L. Nature 614, 224–226 (2023).
Bockting, C. L., van Dis, E. A. M., van Rooij, R., Zuidema, W. & Bollen, J. Nature 622, 693–696 (2023).
Lee, P., Goldberg, C. & Kohane, I. The AI Revolution in Medicine: GPT-4 and Beyond (Pearson, 2023).
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).
Lobentanzer, S., Rodriguez-Mier, P., Bauer, S. & Saez-Rodriguez, J. Mol. Syst. Biol. 20, 848–858 (2024).
Chakravarty, D. et al. JCO Precis. Oncol. https://doi.org/10.1200/po.17.00011 (2017).
Camacho, C. et al. BMC Bioinformatics 10, 421 (2009).
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.
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Authors and Affiliations
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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.
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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.
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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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DOI: https://doi.org/10.1038/s41587-024-02534-3
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