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Updated: Jun 11, 2025

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泛基因组信息化语言模型用于保护隐私的合成基因组序列生成
Pengzhi Huang1, François Charton2, Jan-Niklas M Schmelzle3,1
1Cornell University.
bioRxiv : the preprint server for biology
|October 10, 2024
概括
我们开发了一种新的方法,使用泛基因组和预训练的语言模型生成合成DNA数据,通过差异隐私 (DP) 确保个人隐私. 这种方法增强了DNA建模,并保持了研究数据的实用性.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 公共基因组数据集 (例如,人类基因组项目) 加快了研究.
- 在基因组数据共享中保护个人隐私至关重要.
- 对于复杂的基因组结构,目前用于合成数据生成的方法需要改进.
研究的目的:
- 使用泛基因组和预训练语言模型 (PTLM) 来生成保护隐私的合成DNA数据.
- 开发和评估用于增强DNA建模的新型代币化方案.
- 确保生成的数据符合差异性隐私 (DP) 标准.
主要方法:
- 使用泛基因组图和PTLM用于合成DNA数据生成.
- 推出了两种基于泛基因组的新型代币化方案.
- 与使用单核酸和k-mer方法的新型代币化进行了比较.
- 将差异隐私 (DP) 技术应用于 pangenome 图和训练过程.
主要成果:
- 与经典方法相比,新型代币化方案改善了模型性能的一致性和有效的上下文长度.
- 拟议的DP方法允许对基因组图进行符合隐私的使用.
- 在DP训练模型中评估了隐私和准确性之间的权衡.
结论:
- 使用 PTLM 的基于 pangenome 的代码化为保护隐私的合成 DNA 数据生成提供了一个有前途的方法.
- 开发的方法增强了DNA序列的建模,同时遵守DP标准.
- 这项工作促进了下游分析基因组信息的安全共享.
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