针对多主题的联合学习:帕金森病的绩效评估
Benjamin Danek1,2,3, Mary B Makarious4,5,6, Anant Dadu2,3
1Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA.
bioRxiv : the preprint server for biology
|November 21, 2023
概括
联合学习 (FL) 对训练机器学习 (ML) 模型在多omics帕金森病数据上显示出希望,实现接近中央训练模型的性能. 数据分散影响FL模型的有效性.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 机器学习 机器学习
背景情况:
- 在omics中的机器学习 (ML) 应用受到数据访问的限制.
- 联合学习 (FL) 为协作,保护隐私的数据策划提供了一个解决方案.
结论:
- FL是多学科研究的可行方法,克服了数据访问的限制.
- 开源的FL框架可以有效地用于协作研究.
- 需要进一步的研究来优化FL策略,以应对OMIC数据挑战.
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