Fusion_f5C-Pred:一个双分支的特征融合框架,用于预测5-甲基细胞因基因修饰站点的预测
Cong Hui1, Junhao Yu2, Jianhua Jia3
1School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China. 3539393405@qq.com.
BMC genomics
|November 27, 2025
概括
我们开发了Fusion_f5C-Pred,这是一种深度学习工具,可以预测5-formylcytidine (f5C) RNA的修饰,对于线粒体功能和潜在的癌症转移至关重要. 这种方法显著提高了对现有工具的预测准确性.
科学领域:
- 分子生物学分子生物学
- 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:
- 5-甲基丁 (f5C) 是线粒体蛋白质合成和翻译调节中的关键RNA修饰.
- f5C 修改涉及通过线粒体mRNA转化促进癌症转移.
- 目前f5C站点的计算预测方法有限,阻碍了研究.
研究的目的:
- 为了解决预测f5C修饰地点的局限性.
- 开发一个先进的计算工具来识别f5C修改.
- 为了促进对f5C.分子功能的更深入的研究.
主要方法:
- 开发了Fusion_f5C-Pred,一个双分支深度学习框架.
- 集成序列和结构性RNA特征使用封闭的融合网络.
- 采用密集连接的卷积网络,注意序列和变压器编码器的结构.
主要成果:
- 在独立的数据集上,Fusion_f5C-Pred实现了高预测性能 (ACC 0.7952,AUROC 0.8684).
- 该模型显著优于现有的预测方法.
- 功能融合增强了类间的分离,表明了强大的表示.
结论:
- Fusion_f5C-Pred为f5C研究提供了一个强大的计算工具.
- 该模型的学习模式显示了生物解释性,与已知的RNA动机保持一致.
- 该框架为研究其他RNA修饰提供了可转移的架构.
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