XCNN-SC:可解释的CNN对SARS-CoV-2变种的分类和突变检测检测
Elmira Yektadoust1, Amin Janghorbani1, Ahmad Farhad Talebi1
1Biotechnology Department, Faculty of New Sciences and Technologies, Semnan University, Semnan, Iran.
Computers in biology and medicine
|November 4, 2024
概括
一个深度学习模型使用基因组数据准确地分类了七种严重急性呼吸综合征冠状病毒2 (SARS-CoV-2) 变体. 这种方法还确定了关键突变,有助于理解变体进化和功能差异.
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.
- 机器学习 机器学习
背景情况:
- 在全球范围内,COVID-19的流行病造成了数百万人的死亡.
- 在SARS-CoV-2中高突变率挑战了疫苗的有效性和控制策略.
- 准确识别和分类SARS-CoV-2变种对于公共卫生至关重要.
研究的目的:
- 开发一种机器学习模型,用于分类七种SARS-CoV-2变种 (阿尔法,贝塔,三角形,埃塔,埃普西隆,洛塔,奥米克朗).
- 通过模型识别的病毒基因组内的歧视性区域进行调查.
- 检测突变并了解它们对不同SARS-CoV-2变种的功能影响.
主要方法:
- 从NCBI数据库中获得了82,802个完整的SARS-CoV-2基因组.
- 应用标签编码来将基因组序列转换为数值数据.
- 使用单层1D卷积神经网络 (1D-CNN) 进行变异分类和特征图分析.
主要成果:
- 1D-CNN模型实现了高性能:99.78%的准确度,97.98%的精度,97.66%的灵敏度,99.95%的特异性和98.68%的F1-score.
- 对特征图的分析确定了对变体歧视至关重要的序列区域.
- 检测到三种突变,包括之前报告的一种错误感应突变和两种新的无声突变.
结论:
- 1D-CNN模型是有效的基于基因组序列的SARS-CoV-2变体的歧视.
- 美国有线电视新闻网 (CNN) 的特征地图分析为理解学习模式提供了一种可解释的方法.
- 这种方法有助于检测突变并阐明它们在变异中的功能影响.
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