基因组和遗传任务的基因组基础模型的基准测试
Haonan Feng1, Lang Wu2, Bingxin Zhao3
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Nature communications
|November 28, 2025
概括
评估DNA基础模型对于基因组学至关重要. 我们的基准测试表明,代币嵌入增强了序列分类,但模型性能在基因组任务之间有所不同,指导了未来的选择.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- DNA基础模型正在快速推进基因组学.
- 目前缺乏对这些模型的全面评估.
- 了解不同任务中的模型性能是必不可少的.
研究的目的:
- 提供五种领先的DNA基础模型的全面,公正的基准.
- 用零射击嵌入来评估各种基因组和遗传任务的模型性能.
- 为不同的基因组应用确定最佳的嵌入策略.
主要方法:
- 他们对五种DNA基础模型进行了基准测试:DNABERT-2,核酸转换器V2,HyenaDNA,Caduceus-Ph和GROVER.
- 评估了序列分类,基因表达预测,变异效应量化和TAD区域识别的模型.
- 利用零射击嵌入,并比较各种聚合策略,重点关注平均代币嵌入.
主要成果:
- 与其他策略相比,嵌入平均代币显著改善了序列分类性能.
- 一般用途的DNA基础模型在致病变体鉴定中表现良好.
- 与专业方法相比,模型在预测基因表达和识别因果QTL方面存在局限性.
结论:
- 模型性能取决于任务和数据集.
- 平均代码嵌入是一种强大的策略,用于序列分类.
- 研究结果为根据任务要求,架构,预训练数据和嵌入策略选择DNA基础模型提供了一个框架.
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