基于Mamba-VMD的新型基因序列预测和同型加密方法的研究
Xiaoyong Liu1, Zhuhui Tan2, Peikang Tang3
1School of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin, Guangxi 541006, China; Guangxi Engineering Research Center for Optoelectronic Information and Intelligent Communication Technology, Guilin University of Technology, Guilin, Guangxi 541006, China.
Computational biology and chemistry
|November 15, 2025
概括
这项研究引入了一种安全的基因序列预测方法,使用Mamba神经网络和同型加密. 这种方法在云传输和分析期间保护敏感的生物信息数据.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 基因序列预测对于理解基因功能和进化至关重要.
- 将敏感的基因序列在纯文本中传输到云环境会危及隐私.
- 现有的方法对于基于云的生物信息学数据分析缺乏强大的安全性.
研究的目的:
- 在云环境中开发一种安全和准确的基因序列预测方法.
- 将Mamba神经网络与同型加密集成,用于隐私保护分析.
- 通过使用病毒基因序列数据来验证拟议的方法.
主要方法:
- 使用Mamba神经网络进行基因序列预测.
- 用于预处理基因序列的VMD模式分解.
- 同型加密 (CKKS) 用于安全的云数据处理.
- 在加密数据上的空间相似性分析.
主要成果:
- 实现了较低的预测错误 (MAE:0.0140,MSE:0.0003,RMSE:0.0190,MAPE:0.6527,MSPE:789.52959).这些错误在预测过程中是非常小的.
- 显示了低平均 CKKS 同型加密计算错误 (0.582492456).
- 成功启用云中的基因序列数据的安全相似性计算.
结论:
- 结合的Mamba神经网络和同型加密方法确保了数据隐私.
- 该方法提供了准确的基因序列预测,同时保持安全性.
- 该框架适用于在云环境中进行敏感生物信息学数据分析.
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