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横向基因转移检测的现状和未来前景

Andre Jatmiko Wijaya^1,2,3, Aleksandar Anžel¹, Hugues Richard³

¹Center for Artificial Intelligent in Public Health Research (ZKI-PH), Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany.

NAR genomics and bioinformatics

|February 12, 2025

概括

人工智能 (AI) 越来越多地用于检测水平基因转移 (HGT),这是抗菌素耐药性 (AMR) 的关键因素. 这篇评论分析了计算方法,并强调了AI.

科学领域:

生物信息学是一种生物信息学.
进化生物学进化生物学
计算生物学计算生物学

背景情况:

水平基因转移 (HGT) 驱动 prokaryotic 进化,并与抗菌素耐药性 (AMR) 有关.
有许多用于HGT检测的计算方法,但人工智能 (AI) 的作用仍未得到充分探索.
抗菌素耐药性 (AMR) 构成了全球重大公共卫生威胁,使得HGT检测至关重要.

研究的目的:

审查当前用于水平基因转移 (HGT) 检测的计算方法.
调查人工智能 (AI) 在HGT检测领域的新兴应用.
提供关于HGT检测方法的演变和未来研究方向的见解.

主要方法:

对HGT检测计算方法的系统文献综述.
将现有方法分类为基于计算技术的层次结构.
分析趋势,包括整合基于人工智能的方法.

主要成果:

显而易见的是,对HGT检测的兴趣日益增长,最近计算方法的激增,包括基于AI的方法.
组织了现有的HGT检测计算方法,并绘制了它们的进化进展.
审查确定了在HGT检测中采用AI的关键挑战和机会.

结论:

人工智能为推进HGT检测方法提供了一个有希望的途径.
需要进一步的研究来充分探索和优化人工智能应用,以了解进化动态和打击AMR.
解决HGT检测方面的挑战,特别是人工智能集成,对于公共卫生的进步至关重要.

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