DeepCIP:一种多式深度学习方法,用于预测circRNAs的内部核糖体进入点
Yuxuan Zhou1, Jingcheng Wu2, Shihao Yao3
1Innovation Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang University Innovation Institute for Artificial Intelligence in Medicine - Aoming (Hangzhou) Biomedical Co., Ltd. Joint Laboratory, Hangzhou, 310018, China.
Computers in biology and medicine
|August 5, 2023
概括
DeepCIP是一种新的深度学习工具,通过整合序列和结构数据,准确地预测圆形RNA (circRNAs) 中的内部核糖体进入点 (IRES). 这一进步有助于理解circRNA编码潜力和开发新疗法.
科学领域:
- 计算生物学 计算生物学
- 分子生物学分子生物学
- 基因组学就是基因组学.
背景情况:
- 循环RNAs (circRNAs) 可以通过内部核糖体进入点 (IRES) 编码蛋白质,从而实现独立于帽子的翻译.
- 在circRNA中识别IRES元素对于阐明它们的生物功能至关重要.
- 现有的IRES预测方法主要设计用于线性RNA,限制其应用到circRNA.
研究的目的:
- 开发一种高精度的计算工具,用于预测IRES元素,特别是在circRNA中.
- 利用多模式深度学习,整合序列和结构RNA信息.
- 增强对circRNA翻译和编码潜力的理解.
主要方法:
- 提出了DeepCIP,这是一个用于circRNA IRES预测的多式联络深度学习框架.
- 利用序列组成和RNA二次结构信息作为输入特征.
- 集成了一个可解释的分析机制来识别关键序列模式.
主要成果:
- 与现有方法相比,DeepCIP在预测circRNA IRES元素方面表现优越.
- 序列和结构信息的整合显著提高了预测准确性.
- 可解释性分析揭示了与已知的动机相一致的序列模式,以促进circRNA翻译.
结论:
- 通过结合序列和结构数据,DeepCIP有效地预测circRNA IRES元素.
- 该工具为研究circRNA翻译和编码潜力提供了宝贵的资源.
- DeepCIP对开发基于circRNA的疗法有影响.
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