联邦学习助力生物信息学跨机构协作，保护隐私同时提升研究效率
Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities
- 通过跨机构数据协作训练模型，避免原始数据共享
- 在蛋白质组学、基因组关联研究等领域已有实际应用
- 适合关注隐私保护与多组学数据融合的研究者
联邦学习在生物信息学中可实现跨机构数据协作，推动临床发现，同时满足数据共享限制并保护患者隐私。本文首次系统综述了其在蛋白质组学、全基因组关联研究（GWAS）、单细胞及多组学研究中的应用，涵盖方法学、基础设施和法律层面的挑战。随着遗传学与系统生物学中生物样本库的发展表明，获取更广泛多样化的数据能加速结果探索与转化。更广泛采用联邦学习有望在生物信息学领域产生类似影响，使学术与临床机构能够访问现有生物样本库中未覆盖或缺失的基因型、表型及环境信息组合。
原文摘要 · Abstract (English)
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.
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