绘制病毒圈:人工智能和生物信息学在病毒发现和进化中的计算协同作用
Aia Sinno1, Ruqaya Baghdadi1, Ralph Narch1
1Department of Biological Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon.
Journal of virology
|November 12, 2025
概括
人工智能 (AI) 增强了超越序列相似性的病毒发现,克服了传统方法的局限性. 将人工智能与经典生物信息学相结合,为可扩展和可解释的病毒检测和进化见解提供了一个统一的战略.
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:
- 大基因组测序揭示了病毒的多样性,但基于同质的工具与不同的基因组作斗争.
- 人工智能 (AI) 和机器学习模型为病毒发现提供了新的方法.
- 基于结构的框架和图形神经网络推进了蛋白质注释和宿主病毒相互作用建模.
研究的目的:
- 审查计算病毒学从传统到人工智能驱动的方法的演变.
- 检查具有里程碑意义的AI工具及其在病毒发现方面的挑战.
- 提出一个集成的计算工作流,结合人工智能和古典生物信息学.
主要方法:
- 审查人工智能 (AI) 模型,包括CNN,RNN和变压器.
- 分析基于结构的框架,如AlphaFold和ESMFold.
- 探索图形神经网络 (GNN) 和可解释的AI.
主要成果:
- 人工智能模型将病毒发现扩展到序列相似度之外.
- 基于结构的工具可以对不同的病毒蛋白进行注释.
- 人工智能方法提出了包括计算负担和数据偏差在内的挑战.
结论:
- 将AI模式识别与经典生物信息学结合在一起的集成工作流可以提高可扩展性和可解释性.
- 解决纯粹由人工智能驱动或传统方法的局限性至关重要.
- 一个统一的计算策略加速病毒发现和全球准备的进化见解.
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