使用基于长期短期记忆的时间序列模型进行麻基因组突变分析
Refat Khan Pathan1, Mohammad Amaz Uddin2, Ananda Mohan Paul3
1Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Selangor, Malaysia.
PloS one
|August 23, 2023
概括
根据一项分析NCBI数据的新研究,病毒基因突变率正在下降. 这项研究利用深度学习模型来预测未来的麻疹突变.
科学领域:
- 病毒学 病毒学
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
背景情况:
- 麻疹是一种双链DNA病毒,属于Orthopoxvirus属和Poxviridae家族.
- 人与人之间的传播是通过与呼吸道分泌物,受感染的个人/动物或受污染的物体直接接触而发生的.
- 世界卫生组织在2022年7月宣布公共卫生紧急情况,原因是麻疹病例普遍蔓延.
研究的目的:
- 通过使用最新的NCBI数据集,分析病毒的基因突变率.
- 为了独立识别核酸和密码子突变.
- 为了预测麻疹病毒的未来突变率.
主要方法:
- 从NCBI.BI收集并准备的水基因数据.
- 基于地理数据 (加拿大,德国,世界其他地区) 的分类突变率.
- 采用深度学习模型,特别是长短期记忆 (LSTM) 和门式循环单元 (GRU),用于基因组突变率的预测.
主要成果:
- 长期短期记忆 (LSTM) 模型实现了0.09的根平均平方误差 (RMSE) 值,用于测试和0.08用于训练.
- 观察到核酸突变率正在下降.
- 保持了双向突变率之间的平衡.
结论:
- 深度学习模型,特别是LSTM,可以有效地预测麻疹基因组突变率.
- 这项研究为病毒病毒的进化和突变趋势提供了新的见解.
- 观察到的核酸突变率下降表明病毒进化的潜在稳定或转变.
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