使用基于变压器的模型从DNA/RNA序列分类高度分离的病毒
Tariq Sadad1, Raja Atif Aurangzeb2, Mejdl Safran3
1Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan.
Biomedicines
|May 27, 2023
概括
这项研究引入了用于识别病毒DNA/RNA序列的深度学习系统,达到97.69%的准确性. 这种新的方法克服了检测新的和不同的病毒株的传统方法的局限性.
科学领域:
- 计算病毒学 计算病毒学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 病毒对全球健康构成重大风险,导致感染并增加癌症风险.
- 新兴的病毒威胁,如COVID-19和流感,突出了需要先进的检测方法.
- 传统的病毒学与新的或不同的病毒基因组作斗争,需要新的分析方法.
研究的目的:
- 开发一种深度学习系统,用于准确识别病毒DNA/RNA序列.
- 解决传统方法在检测新型和异型病毒方面的局限性.
- 改善病毒病原体,变种和菌株的分化.
主要方法:
- 利用先进的深度学习,特别是用于DNA分析的自定义BERT架构.
- 使用NCBI GenBank的核酸序列和BERT令牌器进行特征提取.
- 嵌入式合成数据生成对于具有有限样本大小的病毒.
- 开发了一个由两个组成部分组成的系统:无监督下一个codon学习和基因型-表型分类器.
主要成果:
- 在识别病毒序列方面达到97.69%的高精度.
- 展示了系统识别数十种不同病毒的能力.
- 通过使用深度学习,成功地从核酸序列中提取了特定域的特征.
结论:
- 拟议的深度学习系统为病毒序列识别提供了强大而准确的方法.
- 这种计算病毒学方法增强了病原体检测,有助于公共卫生监测.
- 该系统有效地应对病毒基因组变异性和有限数据带来的挑战.
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