ncProFormer:用于ncRNA编码-潜在预测的CNN增强型变压器
Mengyu Tong1,2, Yuanting Chen1,2, Long Chen2
1State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Journal of chemical information and modeling
|March 14, 2026
概括
我们开发了ncProFormer,这是一个深度学习工具,用于识别能够编码的非编码RNA (ncRNA). 这个框架准确地预测了微的功能,超过了现有的方法,并证明了跨物种的适用性.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 非编码RNAs (ncRNAs) 可以转化为功能性微,但识别能够编码的ncRNAs是具有挑战性的.
- 弱的翻译信号,低的保存率和数据异质性使准确的预测变得复杂.
研究的目的:
- 开发一个先进的深度学习框架,ncProFormer,用于预测ncRNAs的编码潜力.
- 提高识别功能性微编码ncRNA的准确性和通用性.
主要方法:
- ncProFormer集成了GENA-LM核酸语言模型用于上下文序列嵌入.
- 一个卷积神经网络 (CNN) 增强的变压器编码器捕获本地和远程序列依赖.
- 该框架使用全代币表示策略进行全面分析.
主要成果:
- ncProFormer在人类,外部验证和公共基准数据集上显著优于现有的方法.
- 该模型在没有重新训练的情况下,在物种 (人类,老鼠,老鼠) 中表现出强大的预测性能.
- 这突显了学习到的生物表征的可转移性和对分布变化的稳定性.
结论:
- ncProFormer是一个有效和可泛化的深度学习框架,用于识别编码能力强的ncRNA.
- 该工具提供了一种有前途的计算方法,用于在各种转录学背景下表征ncRNA函数.
- 这项工作推进了ncRNA研究领域,通过提供一种可靠的方法来发现新的微编码ncRNA.
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