在米基因组中识别DNAN6-甲基胺修饰,使用微调的大型语言模型进行识别
Yichi Zhang1, Hao Chen1, Shicheng Xiang1
1College of Biomedical Engineering, Sichuan University, Chengdu, China.
Frontiers in plant science
|July 10, 2025
概括
研究人员开发了iRice6mA-LMXGB,这是一种使用微调的大型语言模型和XGBoost精确识别大米中DNAN6-甲基胺 (6mA) 位点的新方法. 这一进步有助于理解大米基因调节和表观遗传学.
科学领域:
- 表观遗传学和基因组学
- 计算生物学 计算生物学
- 植物科学 植物科学
背景情况:
- DNA N6-甲基氨酸 (6mA) 是大米基因组的关键表观遗传修饰,影响生长,发育和基因表达.
- 准确识别6mA位点对于破译大米基因表达调节网络至关重要.
- 现有的6mA位点识别预测模型需要进一步提高准确性和效率.
研究的目的:
- 开发一种先进的计算模型,精确识别大米基因组中的6mA位点.
- 利用大型语言模型和机器学习来加强大米的表观基因组分析.
- 为研究大米表观遗传学和基因调节的研究人员提供一个强大的工具.
主要方法:
- 拟议的iRice6mA-LMXGB架构集成了一个微调的BERT模型 (初始化为DNABERT-2) 用于特征提取.
- 利用XGBoost模块根据提取的DNA序列特征对6mA位点进行分类.
- 员工转移学习和UMAP可视化以验证微调策略和模型性能.
主要成果:
- 在五倍交叉验证设置中实现了0.9903的高验证精度.
- 在接收器操作特征 (ROC) 曲线上获得0.9994的曲线下的面积 (AUC).
- 与同一个数据集上的现有预测指标相比,表现优越.
结论:
- 该iRice6mA-LMXGB方法在识别米6mA位点方面取得了重大进展.
- 微调的大型语言模型和XGBoost的集成为表观遗传学研究提供了一个强大而准确的工具.
- 这项研究为大米表观遗传学和基因表达调节提供了宝贵的见解.
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