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预测病毒的遗传进化,用人工智能识别合适的疫苗

Osama R Shahin¹, Mohamed N Ibrahim², Awadh Alanazi³

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia. orshahin@ju.edu.sa.

Scientific reports

|February 3, 2026

概括

一个新的精细的深度进化学习框架 (R-DELF) 预测病毒突变和疫苗适用性. 这种人工智能方法通过预测高风险变体和改进疫苗开发时间表,提高了疫情准备.

科学领域:

病毒学病毒学
基因组流行病学基因组流行病学
人工智能的人工智能

背景情况:

病毒进化对疫苗开发构成全球性挑战,原因是免疫逃脱和现有疫苗的有效性降低.
传统的基因组流行病学方法是回顾性的,不能预测未来的病毒进化趋势.

研究的目的:

引入一种新的AI框架,即精制的深度进化学习框架 (R-DELF),用于积极预测病毒突变.
使用基因组,结构和时间数据评估疫苗对新兴病毒变异的适用性.

主要方法:

R-DELF框架集成了一个ESM-2变压器用于结构意识嵌入和双重注意力图形神经网络 (GNN) 用于基因和结构依赖性学习.
一个进化学习最大化器增强了适应模型,并补充了一个可解释的AI层,用于残留级别的归因和可解释性.
该框架在Python中使用TensorFlow实现,利用基因组和蛋白质数据.

主要成果:

R-DELF实现了高实验准确度 (99.2%),精度 (97.92%),回忆 (98.89%) 和F1得分 (99.4%),优于目前基于AI的病毒学模型.
该框架证明了能够提前预测高风险突变的能力.

结论:

在预测病毒演变和评估疫苗适用性方面,R-DELF提供了显著的进步.
该框架通过及时生产疫苗和对病毒进化轨迹的数据驱动预测来增强流行病的准备.

关键词:

人工智能的人工智能是人工智能.基因预测的基因预测基因组分析机器学习疫苗的研发工作正在进行中.病毒的进化是病毒的进化.

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