基因组序列分类半监督学习中的 DNA 数据增强：基于 GROVER 的 FixMatch/FlexMatch 弱强增强配方研究
DNA augmentations for semi-supervised learning in genomic sequence classification
一项应用研究系统评估了面向伪标签半监督学习的基因组感知弱—强增强配方，在 FixMatch 与 FlexMatch 框架下以 GROVER DNA 语言模型为骨干，测试了 25 组弱/强增强组合。评测覆盖两个分类基准：基于 AMR 基因序列的抗菌药物类别预测，以及基于模拟 150 bp 肿瘤样 reads 的多类肿瘤病毒家族分类。结果显示，适度且保持序列的扰动在准确率与稳定性之间取得最佳平衡；过度掩码或高强度突变会增大方差并降低性能；以短片段掩码（NN）作弱增强、约 5% 的 indel 率作强增强是稳健策略。作者公开了实现与推荐默认参数，以支持有限标签条件下的可复现半监督基因组序列建模。
来源：NAR Genomics and Bioinformatics · doi.org
Semi-supervised learning (SSL) is a natural fit for genomics, where unlabeled sequences are abundant but high-quality labels are scarce due to costly curation. However, most SSL pipelines rely on generic perturbations that are not tailored to biological constraints, and it remains unclear which augmentation designs yield reliable gains for DNA sequence classification. In this application note, we present an empirical study of genome-aware weak–strong augmentation recipes for pseudo-label-based SSL, focusing on FixMatch and FlexMatch with a GROVER DNA language model backbone. Across two classification benchmarks—antimicrobial resistance (AMR) drug-class prediction from AMR gene sequences and multi-class oncovirus family classification from simulated 150 bp tumor-like reads—we systematically evaluate 25 weak/strong augmentation pairs constructed from biologically motivated operators, including reverse-complement, codon-level back-translation, mild masking/substitution (NN), high-rate substitution, and length-preserving insertion–deletion (indel). We find consistent trends: (i) moderate, sequence-preserving perturbations yield the best accuracy–stability trade-off, (ii) excessive masking or aggressive mutation increases variance and degrades performance, and (iii) short-segment masking (NN) for weak augmentation followed by an indel rate (around 5%) for strong augmentation emerge as a robust strategy to learn from unlabeled genomic data. We release implementations and recommended defaults to facilitate reproducible SSL for genomic sequence modeling under limited-label regimes.