发表机构
North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对植物BGC标签稀缺问题，提出PlantBGC模型，通过无标签领域自适应与弱监督实现植物BGC的精准发现，提升了已知BGC的恢复率并缩短了基因座长度。
AI 中文摘要
植物生物合成基因簇（BGC）编码特殊代谢物通路，但经过整理的植物BGC标签十分稀缺，阻碍了基因组规模的监督式发现。现有的植物BGC挖掘工具大多基于特征和规则驱动，未能充分利用近期上下文表示学习的进展来建模长程领域上下文，以及在强领域偏移下控制假阳性。我们旨在构建一种AI辅助工作流，通过将监督信号从标注完善的微生物BGC迁移至植物基因组，缩小实验搜索空间。我们提出PlantBGC，将基因组表示为有序的Pfam结构域序列，利用仅编码器Transformer学习BGC相似性，该模型在MIBiG微生物BGC上训练，并通过无标签掩码语言建模适配至植物。在微生物基准测试中，PlantBGC的token级AUC达到0.988（10折交叉验证）和0.979（留类验证）；在植物中，适配后对34个经整理的基因座的已知BGC恢复率在严格100%覆盖下从29.4%提升至67.6%，表明边界更完整。源自GO/KEGG的弱监督将代理初级样比例分别降低48.40%（GO）和45.20%（KEGG），各物种均呈现一致降低（配对Wilcoxon检验p=1.53e-5）；与plantiSMASH相比，PlantBGC在匹配区域产生更紧凑的基因座（中位长度比为0.278，93.8%的对更短）。
英文摘要
Plant biosynthetic gene clusters (BGCs) encode specialized-metabolite pathways, yet curated plant BGC labels remain scarce, hindering supervised discovery at genome scale. Existing plant BGC mining tools are largely signature- and rule-driven and do not fully leverage recent advances in contextual representation learning for modeling long-range domain context and controlling false positives under strong domain shift. We seek an AI-assisted workflow that narrows experimental search space by transferring supervision from well-annotated microbial BGCs to plant genomes. We present PlantBGC, representing genomes as ordered Pfam-domain sequences and learning BGC-likeness with an encoder-only Transformer trained on MIBiG microbial BGCs and adapted to plants via label-free masked language modeling. On microbial benchmarks, PlantBGC achieves token-level AUC = 0.988 (10-fold CV) and 0.979 (leave-class-out). On plants, adaptation improves known-BGC recovery on n = 34 curated loci under strict 100% coverage, increasing recovery from 29.4% to 67.6% and indicating more complete boundaries. GO/KEGG-derived weak supervision reduces proxy primary-like ratio by 48.40% (GO) and 45.20% (KEGG), with consistent per-species reductions (paired Wilcoxon p = 1.53e-5). Compared to plantiSMASH, PlantBGC yields more compact loci on matched regions (median length ratio = 0.278; 93.8% of pairs are shorter).
