用进化树训练的Transformer模型，预测新冠突变更准。
PETRA: Pretrained Evolutionary Transformer for SARS-CoV-2 Mutation Prediction
- 基于进化树而非原始基因序列建模，减少测序噪声
- 预测突变准确率比基线提升近10倍（核酸9.45%）
- 适合疫情监测和疫苗研发人员快速追踪变异
自出现以来，新冠病毒展现出快速且不可预测的演化轨迹，持续产生免疫逃逸变异株，对公共卫生和疫苗研发构成持续挑战。尽管大规模生成式预训练变换器（GPT）已革新序列数据建模，但其直接应用于嘈杂的病毒基因组序列仍受限。本文提出PETRA（Pretrained Evolutionary TRAnsformer），一种基于系统发育树推导的进化轨迹构建的新型Transformer方法，有效缓解测序噪声并捕捉病毒演化的层级结构。通过加权训练框架解决全球序列数据在地理与时间上的显著不平衡问题，PETRA在预测未来新冠病毒突变方面表现优异，核酸突变加权召回率@1达9.45%，氨基酸突变达17.10%，分别较最优基线提升至0.49%和6.64%。此外，PETRA还能实时辅助预测如24F(XEC)、25A(LP.8.1)等主要谱系的突变。代码已开源：https://github.com/xz-keg/PETra。
原文摘要 · Abstract (English)
Since its emergence, SARS-CoV-2 has demonstrated a rapid and unpredictable evolutionary trajectory, characterized by the continual emergence of immune-evasive variants. This poses persistent challenges to public health and vaccine development. While large-scale generative pre-trained transformers (GPTs) have revolutionized the modeling of sequential data, their direct applications to noisy viral genomic sequences are limited. In this paper, we introduce PETRA(Pretrained Evolutionary TRAnsformer), a novel transformer approach based on evolutionary trajectories derived from phylogenetic trees rather than raw RNA sequences. This method effectively mitigates sequencing noise and captures the hierarchical structure of viral evolution. With a weighted training framework to address substantial geographical and temporal imbalances in global sequence data, PETRA excels in predicting future SARS-CoV-2 mutations, achieving a weighted recall@1 of 9.45% for nucleotide mutations and 17.10\% for spike amino-acid mutations, compared to 0.49% and 6.64% respectively for the best baseline. PETRA also demonstrates its ability to aid in the real-time mutation prediction of major clades like 24F(XEC) and 25A(LP.8.1). The code is open sourced on https://github.com/xz-keg/PETra
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