一个基础语言模型来破译RNAs的多样化调节
Hanwen Zhou1,2, Yue Hu3,4, Yulong Zheng1
1Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Genome biology
|September 24, 2025
概括
我们开发了LAMAR,这是RNA调节的语言模型,在预测RNA处理和识别调节元素方面表现优于现有的方法. 这推动了RNA生物学和RNA疗法的设计.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- RNA代谢依赖于cis元素和跨作用因子,调节信息编码在RNA序列中.
- 从序列中预测RNA调节是具有挑战性的,但对于RNA生物学和治疗学至关重要.
研究的目的:
- 开发一个基础语言模型来破译一般的RNA处理规则.
- 为了提高各种RNA调节任务的预测准确性.
主要方法:
- 使用基于变压器的基础语言模型 (LAMAR) 在广泛的哺乳动物和病毒RNA序列上进行预训练.
- 针对特定RNA调节预测任务的标记数据集上微调的LAMAR.
主要成果:
- 在预测mRNA翻译效率和半衰期方面,LAMAR模型超过了最先进的方法.
- 在预测mRNA前拼接部位和内部核糖体进入部位 (IRES) 方面取得了高准确性.
- 确定了影响RNA降解的新型cis调节元件,并发现了用于circRNA翻译的高活性IRES.
结论:
- 一个单一的基础语言模型可以全面分析RNA调节,并识别新的监管元素.
- LAMAR为设计和优化基于RNA的药物提供了洞察力.
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