ProkBERT PhaStyle:使用预训练的基因组语言模型准确预测菌体的生活方式
Judit Juhász1, Noémi Ligeti-Nagy2, Babett Bodnár1
1Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest 1083, Hungary.
Bioinformatics advances
|November 13, 2025
概括
基因组语言模型 (LMs) 从碎片化的序列有效预测菌体的生活方式 (毒性或温和). ProkBERT PhaStyle 展示了高精度和速度,为生态和临床应用提供了更简单的替代方案.
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:
- 菌体的生活方式预测 (毒性与温和性) 对生物医学和生态研究至关重要.
- 环境菌素序列通常是碎片化的,这挑战了传统的依赖数据库的预测方法.
- 现有的计算方法需要广泛的更新和专业知识.
研究的目的:
- 评估基因组语言模型 (LMs) 的有效性,用于从核酸序列直接对菌体生活方式进行分类.
- 为了比较训练有素的LM的性能与现有的最先进的方法.
主要方法:
- 在短,碎片化的菌体序列的数据集上训练三个基因组LM (DNABERT-2,核酸转换器,ProkBERT).
- 基准测试模型的准确性,预测速度和概括能力与专用的菌体生活方式预测工具相比.
主要成果:
- 在各种场景中,ProkBERT PhaStyle 在各种场景中实现了与最先进的模型相比或超过的准确性.
- 该模型对未见的数据进行了强有力的概括,包括来自极端环境的菌体.
- 观察到ProkBERT PhaStyle的推断速度很高.
结论:
- 基因组LMs为菌体生活方式预测提供了一个简单,计算高效的替代方案.
- ProkBERT PhaStyle 的性能,速度和易用性使其适用于生态和临床应用.
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