Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915624900362240 |
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| author | Malpetti, Daniele Scutari, Marco Gualdi, Francesco van Setten, Jessica van der Laan, Sander Haitjema, Saskia Lee, Aaron Mark Hering, Isabelle Mangili, Francesca |
| author_facet | Malpetti, Daniele Scutari, Marco Gualdi, Francesco van Setten, Jessica van der Laan, Sander Haitjema, Saskia Lee, Aaron Mark Hering, Isabelle Mangili, Francesca |
| contents | Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle introduction to this approach in bioinformatics, and is the first to review key applications in proteomics, genome-wide association studies (GWAS), single-cell and multi-omics studies in their legal as well as methodological and infrastructural challenges. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have a similar impact in bioinformatics, allowing academic and clinical institutions to access many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities Malpetti, Daniele Scutari, Marco Gualdi, Francesco van Setten, Jessica van der Laan, Sander Haitjema, Saskia Lee, Aaron Mark Hering, Isabelle Mangili, Francesca Other Quantitative Biology Machine Learning Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle introduction to this approach in bioinformatics, and is the first to review key applications in proteomics, genome-wide association studies (GWAS), single-cell and multi-omics studies in their legal as well as methodological and infrastructural challenges. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have a similar impact in bioinformatics, allowing academic and clinical institutions to access many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks. |
| title | Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities |
| topic | Other Quantitative Biology Machine Learning |
| url | https://arxiv.org/abs/2503.09649 |