用大模型把病毒突变看作翻译，预测奥密克戎演化路径
VirusT5: Harnessing Large Language Models to Predicting SARS-CoV-2 Evolution
- 将病毒突变视为文本翻译，用Transformer模型学习演化规律
- 成功识别出多个突变热点区域，具备预测新变异潜力
- 适合病毒学、流行病学研究者，为防控提供新工具
在病毒演化过程中，基因组不同区域受到不同程度的功能约束。结合密码子偏好性和DNA修复效率等因素，这些约束导致基因组或特定基因中出现独特的突变模式。本研究利用大语言模型（LLMs）预测SARS-CoV-2的演化。通过将从一代到下一代的突变过程视为翻译任务，我们训练了一个名为VirusT5的Transformer模型，以捕捉支撑SARS-CoV-2演化的突变模式。评估结果显示，VirusT5能够有效识别突变热点，并探索其预测未来病毒变异的潜力。研究证实了使用大语言模型将病毒演化建模为翻译过程的可行性，首次提出‘突变即翻译’的概念，为应对病毒威胁开辟了新的方法与工具路径。
原文摘要 · Abstract (English)
During a virus's evolution,various regions of the genome are subjected to distinct levels of functional constraints.Combined with factors like codon bias and DNA repair efficiency,these constraints contribute to unique mutation patterns within the genome or a specific gene. In this project, we harnessed the power of Large Language Models(LLMs) to predict the evolution of SARS-CoV-2. By treating the mutation process from one generation to the next as a translation task, we trained a transformer model, called VirusT5, to capture the mutation patterns underlying SARS-CoV-2 evolution. We evaluated the VirusT5's ability to detect these mutation patterns including its ability to identify mutation hotspots and explored the potential of using VirusT5 to predict future virus variants. Our findings demonstrate the feasibility of using a large language model to model viral evolution as a translation process. This study establishes the groundbreaking concept of "mutation-as-translation," paving the way for new methodologies and tools for combating virus threats
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