为抗微生物药物耐药性绘制知识景观和人工智能的新兴趋势:参考文献和可视化分析
Zhongli Wang1,2, Gaopei Zhu3, Shixue Li1,2
1Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
人工智能 (AI) 加快了抗菌素耐药性 (AMR) 研究,提高了药物发现和打击全球健康威胁. 在蛋白质结构预测和抗生素发现等方法中,人工智能的整合显示出了显著的进展和未来的潜力.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球严重的健康威胁.
- 人工智能 (AI) 提供了新的方法来加速对抗AMR的研究和开发.
- 需要对AMR研究中的AI应用有系统地理解,以指导未来的努力.
研究的目的:
- 绘制AMR研究中AI应用知识格局和发展趋势的地图.
- 为未来的研究方向和AMR人工智能战略决策提供基于证据的见解.
- 确定这个跨学科领域的关键研究集群和新兴趋势.
主要方法:
- 在2014-2024年期间,利用Web of Science核心集合对2,408篇出版物的图书统计分析.
- 集成VOSviewer用于网络可视化和CiteSpace用于时间分析.
- 分析共同作者,关键词共同出现和引用影响,以界定研究演变.
主要成果:
- 针对AMR研究的AI每年显著增长,美国,中国和印度是主要贡献者.
- 关键的突破包括AlphaFold用于蛋白质结构预测和深度学习用于抗生素发现.
- 确定了持久的研究集群:败血症,人工神经网络,AMR,抗菌,药物重定位和分子对接.
结论:
- 人工智能对于推动抗微生物药物发现和打击AMR至关重要.
- 人工智能提高了药物发现方法的速度,效率和预测性能.
- 这项研究为未来的AI驱动的AMR研究和干预提供了战略见解.
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