用基因组大型语言模型编辑距离嵌入
Xiang Li1, Ke Chen1, Yijia Zhang2
1Department of Computer Science and Engineering, The Pennsylvania State University.
bioRxiv : the preprint server for biology
|November 19, 2025
概括
通过利用DNA语言模型的新模型LLMED,通过近似编辑距离来增强基因组序列分析. 这种方法改进了现有的机器学习方法,用于类似序列搜索等任务.
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.
- 计算生物学 计算生物学
背景情况:
- 编辑距离对于基因组序列分析至关重要,但计算密集.
- 目前的编辑距离嵌入方法,包括机器学习方法,在准确度上有局限性.
- 基因组语言模型显示出各种序列分析任务的前景.
研究的目的:
- 调查DNA语言模型的使用,以改善编辑距离嵌入.
- 通过序列嵌入引入LLMED,这是一种通过序列嵌入近似编辑距离的新型模型.
- 以对现有的嵌入技术来评估LLMED的性能.
主要方法:
- 在LLMED中,使用对比学习进行培训.
- 该模型使用预训练的基因组大语言模型.
- 嵌入生成以在规范空间中近似编辑距离.
主要成果:
- 与领先的机器学习和基于规则的方法相比,LLMED在近似编辑距离方面表现出卓越的表现.
- 在类似的序列搜索应用中,LLMED显著提高了准确性.
- 实验性比较证实了LLMED的有效性.
结论:
- 可以有效地利用DNA语言模型进行准确的编辑距离近似.
- 在基因组序列分析和相似性搜索方面,LLMED提供了一个有前途的进步.
- 该LLMED方法解决了当前嵌入方法的局限性.
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