AntigenLM: Structure-Aware DNA Language Modeling for Influenza

• URL: http://arxiv.org/abs/2602.09067v1
• Date: Mon, 09 Feb 2026 08:52:04 GMT
• Title: AntigenLM: Structure-Aware DNA Language Modeling for Influenza
• Authors: Yue Pei, Xuebin Chi, Yu Kang,
• Abstract summary: We present AntigenLM, a generative DNA language model pretrained on influenza genomes with intact, aligned functional units.<n>AntigenLM accurately forecasts future antigenic variants across regions and subtypes, including those unseen during training.<n>It also achieves near-perfect subtype classification.
• Score: 5.938702748853349
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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