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Viral genomes exhibit remarkable diversity in size, structure, and composition, influencing their replication strategies and interactions with host cells. These genomes consist of either DNA or RNA and may be linear or circular. Additionally, they can be single-stranded or double-stranded, with each configuration affecting how the virus propagates within a host. RNA viruses, for instance, generally have smaller genomes than DNA viruses, a factor that contributes to their high mutation rates and...
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基于基因组语言模型预测病毒适应的通用情报框架.

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概括

一个新的框架,GIVAL,使用人工智能从遗传序列预测病毒适应,即使有不完整的数据. 这种工具有助于了解流行病的潜力和跟踪病毒演变.

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科学领域:

  • 病毒学 病毒学
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 病毒大流行通常源于适应人类的动物病毒.
  • 从遗传数据中预测病毒适应性是很困难的,因为序列不完整和训练标签有限.
  • 现有的模型在数据不足和盲目序列预测方面扎.

研究的目的:

  • 开发一个半监督的通用情报框架来预测病毒适应 (GIVAL).
  • 为嵌入病毒蛋白序列创建一种新的语言模型 (vBERT).
  • 克服在数据不足的情况下预测病毒适应性的局限性.

主要方法:

  • 开发了GIVAL,这是一个使用语言模型 (vBERT) 嵌入病毒蛋白序列的半监督框架.
  • 使用隐藏的马尔科夫模型上下文化令牌进行预训练的vBERT.
  • 与其他模型 (DNABERT-2, proteinBERT, ESM-2, Transformer, Word2Vec) 进行vBERT的评估,以区分病毒蛋白质.
  • 评估了GIVAL的准确性和容错性,没有足够的培训标签.

主要成果:

  • 在基于标签 (如血清型和突变) 的病毒蛋白质分类方面,vBERT的表现优于现有的模型.
  • 在使用有限或杂的标签的情况下,GIVAL在病毒适应预测中表现出更高的准确性和更好的容错性.
  • 吉瓦尔成功地预测了马类IAV和牛H5N1IAV的人类适应性的增加.
  • 吉瓦尔发现了MERS-CoV类病毒变体向SARS-CoV-2受体的适应转移.
  • GIVAL量化了病毒变体的增量适应,与人类病例增加相关.

结论:

  • 基瓦尔为基于基因型的病毒适应预测提供了一个一般智能框架.
  • 该框架有效地处理不足的标签和盲目序列输入.
  • 吉瓦尔在预测病毒中的其他基因型与表型关系方面具有潜在的应用.