AntigenLM: 用于流感的结构感知DNA语言模型
AntigenLM: Structure-Aware DNA Language Modeling for Influenza
AI总结：
AntigenLM通过结构感知的DNA语言模型预测流感抗原变异，优于传统方法，同时在亚型分类中表现优异。
摘要：
语言模型在序列分析方面已取得显著进展，但DNA基础模型在任务特定方法面前往往落后，原因尚不明确。我们提出了AntigenLM，这是一种基于流感基因组预训练的生成式DNA语言模型，其包含完整且对齐的功能单元。这种结构感知的预训练使AntigenLM能够捕捉进化约束并在不同任务间进行泛化。在时间序列血凝素（HA）和神经氨酸酶（NA）序列上进行微调后，AntigenLM能够准确预测各地区和亚型的未来抗原变异，包括训练过程中未见过的变异，优于基于系统发育和进化的方法。它还实现了近完美的亚型分类。消融研究显示，通过碎片化或洗牌破坏基因组结构会严重降低性能，揭示了在DNA语言模型中保持功能单元完整性的重要性。AntigenLM因此提供了一个强大的框架用于抗原进化预测，并为构建生物基础的DNA基础模型提供了一般原则。
英文摘要：
Language models have advanced sequence analysis, yet DNA foundation models often lag behind task-specific methods for unclear reasons. We present AntigenLM, a generative DNA language model pretrained on influenza genomes with intact, aligned functional units. This structure-aware pretraining enables AntigenLM to capture evolutionary constraints and generalize across tasks. Fine-tuned on time-series hemagglutinin (HA) and neuraminidase (NA) sequences, AntigenLM accurately forecasts future antigenic variants across regions and subtypes, including those unseen during training, outperforming phylogenetic and evolution-based models. It also achieves near-perfect subtype classification. Ablation studies show that disrupting genomic structure through fragmentation or shuffling severely degrades performance, revealing the importance of preserving functional-unit integrity in DNA language modeling. AntigenLM thus provides both a powerful framework for antigen evolution prediction and a general principle for building biologically grounded DNA foundation models.
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