DNA序列分析风景:对DNA序列分析任务类型,数据库,数据集,文字嵌入方法和语言模型进行全面的审查
Muhammad Nabeel Asim1,2, Muhammad Ali Ibrahim1,3, Arooj Zaib3
1German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany.
Frontiers in medicine
|April 23, 2025
概括
本文将人工智能 (AI) 和脱氧核糖核酸 (DNA) 序列分析联系在一起,详细介绍了44个任务和AI方法. 它提供了将AI整合到基因组学中的资源,以获得更快,更准确的遗传见解.
科学领域:
- 基因组学和生物信息学
- 人工智能在生物学中的应用
- 计算生物学 计算生物学
背景情况:
- 脱氧核糖核酸 (DNA) 是基因蓝图,突变导致遗传疾病和癌症.
- 传统的DNA序列分析是劳动密集的,昂贵的,容易出错的.
- 人工智能 (AI) 具有加速DNA序列分析和解释的潜力.
研究的目的:
- 在DNA序列分析中弥合人工智能研究人员和生物学家之间的差距.
- 提供DNA序列分析任务和AI方法的全面概述.
- 促进人工智能融入基因组数据分析.
主要方法:
- 列出44个不同的DNA序列分析任务,并将它们与AI范式 (分类,回归,集群) 结合起来.
- 整合36个生物数据库的信息,用于创建基准数据集.
- 调查39个词嵌入和67个语言模型用于预测管道.
主要成果:
- 对140个基准数据集的详细见解,用于44个DNA序列分析任务.
- 人工智能预测器的性能值,包括词嵌入和语言模型.
- 与表现最好的传统序列编码基于预测器的比较.
结论:
- 人工智能显著提高了DNA序列分析的效率和准确性.
- 这项工作为开发和整合基因组学中的AI工具提供了必要的资源.
- 有效的AI-基因组学整合需要跨学科的理解和可访问的基准.
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