蛋白序列分析格局:对任务类型,数据库,数据集,文字嵌入方法和语言模型进行系统审查
Muhammad Nabeel Asim1,2, Tayyaba Asif3, Faiza Hassan3
1German Research Center for Artificial Intelligence, Kaiserslautern 67663, Germany.
概括
这项研究将蛋白质组学和人工智能 (AI) 联系起来,详细介绍了63个AI驱动的蛋白质序列分析任务,68个数据库和627个数据集. 它加速了AI在生物研究中的应用开发.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 人工智能在生物学中的应用
背景情况:
- 蛋白序列分析对于理解生物过程和遗传疾病至关重要.
- 对于蛋白质分析的湿实验室实验是昂贵的,耗时的,容易出错的.
- 需要将人工智能 (AI) 整合到大规模蛋白质组学中,以进行高效的分析.
研究的目的:
- 在蛋白质序列分析中提供AI驱动的应用程序的全面概述.
- 为研究人员弥合蛋白质组学和人工智能领域之间的差距.
- 促进人工智能驱动的蛋白质序列分析工具的开发.
主要方法:
- 编译了63个不同的蛋白质序列分析任务.
- 68个蛋白质数据库和627个基准数据集的详细信息.
- 在AI应用中使用的25种词嵌入方法和13种语言模型的分析.
主要成果:
- 在63个蛋白质序列分析任务中,一个全面的AI应用目录.
- 促进人工智能研究人员对生物学的理解.
- 确定63个任务的最先进的性能.
结论:
- 这项工作提供了AI在蛋白质序列分析中的整体观点,加速了工具开发.
- 它是人工智能和蛋白质组学研究人员的宝贵资源.
- 这项研究强调了人工智能的潜力,可以彻底改变生物数据分析.
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