通过非IID数据的联合学习进行药物发现的协作分析
Dong Huang1, Xiucai Ye1, Ying Zhang2
1Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
Methods (San Diego, Calif.)
|September 9, 2023
概括
这项研究引入了药物发现的联合学习框架,允许在不共享敏感信息的情况下对非IID数据进行协作模型培训. 该方法确保了数据隐私,并实现了具有竞争力的准确性,推动了大规模的药物发现工作.
科学领域:
- 计算化学和化学信息学
- 药物的发现和开发.
- 在医疗保健领域的机器学习和人工智能.
背景情况:
- 大规模的定量结构-活性关系 (QSAR) 数据集对于药物发现至关重要.
- 对QSAR数据的集中分析面临隐私和安全挑战.
- 联合学习 (FL) 允许在没有原始数据共享的情况下进行协作模型培训,但在使用非独立且相同分布的 (非IID) 数据方面存在困难.
研究的目的:
- 提出一个新的联合学习框架,用于非IID数据集上的协作药物发现.
- 为了实现强大的预测模型的联合培训,同时在多个机构中保持数据隐私.
- 克服FL在药物发现中处理非IID数据方面的局限性.
主要方法:
- 开发了一个联合学习框架,用于协作药物发现.
- 通过在机构之间全球共享一个小数据子集来解决非IID数据挑战.
- 利用FL将模型训练分布在本地设备之间,避免直接的数据交换.
主要成果:
- 与15个基准数据集的集中分析相比,拟的框架实现了竞争性预测准确性.
- 该方法成功地保护了个人机构数据的隐私.
- 证明的好处包括减少数据传输和增强可扩展性.
结论:
- 新的联合学习框架有效地支持非IID数据集上的协作药物发现.
- 该方法平衡了预测性能与基本数据隐私要求.
- 该框架适用于大规模的,保护隐私的药物发现倡议.
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