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预先训练的DNA语言模型嵌入的无监督评估

Raghav Awasthi¹, Gayan Samuditha Mend Mend Arachchige¹, Xiaofeng Zhu²

¹Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Wolstein Research Building, Cleveland, OH, USA.

|August 2, 2025

概括

我们开发了一个新的框架来评估使用无监督指标的DNA语言模型 (DLM). GENA-LM和其他模型表现出强的表现,这表明这些指标可以有效地评估DLM质量.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
机器学习机器学习

背景情况:

DNA语言模型 (DLM) 对遗传学任务有希望,但在个人转录组变异方面存在困难.
目前的评估方法是计算密集型的,不评估通用学习能力.

研究的目的:

引入一个计算效率高的框架来评估DLM嵌入式.
用无监督指标评估六个最先进的DLM的业绩.
探索无监督指标与监督任务执行之间的相关性.

主要方法:

提出了一个使用无监督数值线性代数基准指标的框架:RankMe,NESum和StableRank.
在基因组数据集上生成了六个DLM (核酸转换器,DNA-BERT2,HyenaDNA,MistralDNA,GENA-LM,GROVER) 的嵌入.
评估模型使用无监督指标和监督分类任务.

主要成果:

DLM嵌入是高维和非冗余的,以低的相关性和差异表示.
在不受监督的指标中,GENA-LM 始终表现强,并在受监督的任务中获得了高准确度/F1 评分.
在无监督指标和监督绩效之间观察到正相关性,验证无监督指标作为质量代理.

结论:

引入了用于DLM评估的计算效率高的框架.
GENA-LM,DNA-BERT2和核酸转换器的表现普遍超过了HyenaDNA和MistralDNA.
无监督指标与下游业绩相关,作为DLM质量评估的有效代理.

关键词:

DNA语言模型 DNA语言模型基因组基准数据集模型评估指标模型评估指标监督的分类是监督的分类.没有监督的学习学习.

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