关于基于深度学习的识别和预测长非编码RNA的相互作用机制的综合调查
1Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China.
Briefings in functional genomics
|April 5, 2024
概括
深度学习模型为探索未知长非编码RNA (lncRNA) 测序数据提供了强大的工具. 这些人工智能方法加快了与疾病相关的 lncRNAs 的识别,以获得更好的预后和药物开发.
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.
- 人工智能的人工智能
背景情况:
- 长非编码RNAs (lncRNAs) 是真核生物基因表达的关键调节者.
- 测序和基因组学的进步揭示了 lncRNAs 在生理学和疾病中的作用.
- 大量的lncRNA测序数据仍然未被探索.
研究的目的:
- 对 lncRNA 研究的深度学习 (DL) 模型进行审查.
- 弥合研究人员在lncRNA功能研究中DL应用方面的知识差距.
- 为了指导模型选择和用于lncRNA识别的应用.
主要方法:
- 阐明深度学习概念和算法.
- 对各种DL算法的数据偏好进行分析.
- 在lncRNA研究中对DL模型的综合文献综述 (过去5年).
主要成果:
- 识别流行DL算法及其适用于不同lncRNA数据类型的适用性.
- 最近DL模型对lncRNA函数的预测性表现示例.
- 对当前DL模型的优缺点进行批判性分析.
结论:
- 深度学习显著有助于探索 lncRNA 数据,减少研究时间和成本.
- 人工智能驱动的lncRNA识别有助于疾病预后和有针对性的药物发现.
- 未来的前景强调了先进的DL与新兴的lncRNA研究趋势的整合.
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