从核酸到数字:对RNA特征提取方法进行全面的审查,用于计算建模
Fatemeh Safari1,2, Jai J Tree1, Fatemeh Vafaee1,2,3
1School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
Briefings in bioinformatics
|December 31, 2025
概括
本综述整理了用于机器学习的RNA特征提取方法,强调了特征选择如何影响非编码RNA分析. 它提供了改善RNA研究可重现性和可扩展性的工具.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 机器学习 (ML) 对于分析RNA序列,特别是非编码RNA至关重要.
- 特征提取是一个关键步骤,将生物序列转换为ML模型的数值数据.
- 目前RNA特征提取领域是分散的,缺乏标准化和可访问性.
研究的目的:
- 系统地组织和审查超过25个RNA特征提取策略.
- 进行特征集的比较分析及其对ML模型性能的影响.
- 提供可访问工具的精选列表,以促进RNA研究的实际采用.
主要方法:
- 将特征提取策略系统地组织成基于序列和结构的方法.
- 对不同特征集对机器学习模型性能影响的比较分析.
- 编译用于RNA特征提取的公开可用的软件工具和软件包.
主要成果:
- 超过25种RNA特征提取策略分为基于序列和结构的方法.
- 演示了特征集选择如何显著影响机器学习模型在RNA分析中的性能.
- 识别并列出用于特征提取的可访问的计算工具.
结论:
- 综合功能工程对于RNA研究中有效的机器学习至关重要.
- 标准化和整合方法提高了可复制性,可扩展性和可解释性.
- 本综述为机器学习驱动的RNA分析研究人员提供了宝贵的资源.
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