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Updated: Jul 1, 2025

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Diagonal Method to Measure Synergy Among Any Number of Drugs
Published on: June 21, 2018
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通过联合学习进行多方合作药物发现
Dong Huang1, Xiucai Ye1, Tetsuya Sakurai1
1Department of Computer Science, University of Tsukuba, Tsukuba, 3058577, Japan.
Computers in biology and medicine
|March 1, 2024
概括
这项研究引入了一个联合学习框架,用于预测药物标结合亲和力 (DTA) 和药物相互作用 (DDI). 该方法增强了跨机构的协作模式培训,同时保持数据隐私,优于本地学习方法.
科学领域:
- 药理学和药物发现
- 计算化学计算化学
- 人工智能在医学中的应用
背景情况:
- 准确预测药物向结合亲和力 (DTA) 和药物相互作用 (DDI) 对于药物的疗效和安全至关重要.
- 药理学数据往往在各个机构之间隔离,由于隐私和知识产权问题,阻碍了协作模型的开发.
- 局部学习与孤立的数据限制预测模型的性能.
研究的目的:
- 为协作DTA和DDI预测开发一个新的联合学习 (FL) 框架.
- 使多个机构能够在不共享敏感本地数据的情况下训练预测模型.
- 为了提高预测准确度,同时确保强大的数据隐私.
主要方法:
- 提出了一个联合学习 (FL) 框架,集成安全的多方计算 (MPC).
- 在分布式数据集中使用FL进行协作模型培训.
- 在模型聚合过程中利用MPC来保护数据隐私.
主要成果:
- 拟议的FL框架实现了与集中学习相美的预测性能.
- 这种方法显著优于传统的本地学习方法.
- 在多个机构中展示了有效的协作和数据安全性.
结论:
- 新的FL框架成功地解决了协作DTA和DDI预测的挑战,并增强了数据隐私.
- 这种方法通过允许安全的数据共享和改进模型准确性来加速药物发现.
- FL和MPC的整合为制药研究提供了一个可扩展的解决方案.
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