PCVR:用于DNA序列分类的预训练的上下文化视觉表示
Jiarui Zhou1, Hui Wu2, Kang Du3
1School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, 230026, Anhui Province, China.
BMC bioinformatics
|May 9, 2025
概括
我们开发了一种新的方法,即预训练的上下文化视觉表示 (PCVR),用于DNA序列分类. PCVR使用视觉表示和变压器来捕获全球序列信息,大大提高了准确性,并有助于发现新物种.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 机器学习 机器学习
背景情况:
- DNA序列分类对于遗传分析至关重要,但在传统方法中面临速度和信息丢失的挑战.
- 现有的机器学习技术难以处理长序列或牺牲结构信息.
- 频率混沌游戏表示 (FCGR) 将DNA转换为图像,但往往忽略了远程依赖.
研究的目的:
- 引入一种新的方法,即预训练的上下文化视觉表示 (PCVR),用于增强DNA序列分类.
- 为了利用视觉表示和先进的深度学习来捕捉全球序列背景.
主要方法:
- PCVR使用视觉变压器编码FCGR图像,以提取上下文化的特征.
- 面具式自动编码器用于预训练视觉转换器编码器,确保强大的功能学习.
- 该模型在多个数据集上进行评估,用于DNA序列分类任务.
主要成果:
- 预先训练的PCVR在三个数据集的无监督学习中表现出强的表现.
- 微调的PCVR在超级王国和族群层面的分类准确性方面超过了现有的方法.
- 废除研究验证了视力转换器和蒙面自动编码器预训练的有效性.
结论:
- 通过有效捕获全球信息,PCVR显著提高了DNA序列分类的准确性.
- 该方法在发现新物种方面表现有前途,因为它具有稳定性和分析复杂序列数据的能力.
- PCVR框架和代码是公开可用的,用于进一步的研究和应用.
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