系统生物学和人工智能在干细胞研究和治疗开发中的全球演变和影响:范围审查
Thayna Silva-Sousa1,2,3,4, Júlia Nakanishi Usuda1,2,3,4,5, Nada Al-Arawe1,2,3,4,6,7
1BIH Center for Regenerative Therapies (BCRT), Charité Universitätzsmedizin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), 10117 Berlin, Germany.
Stem cells (Dayton, Ohio)
|September 4, 2024
概括
系统生物学 (SysBio) 和人工智能 (AI) 在过去二十年中显著推进了干细胞 (SC) 研究. 由人工智能驱动的研究,包括机器学习 (ML) 和深度学习 (DL),显示出指数式增长,特别是自2010年以来.
科学领域:
- 生物信息学是一种生物信息学.
- 干细胞研究 干细胞研究
- 计算生物学 计算生物学
背景情况:
- 先进的生物信息学,包括系统生物学 (SysBio) 和人工智能 (AI) 方法,如机器学习 (ML) 和深度学习 (DL),越来越多地用于干细胞 (SC) 研究.
- 目前,在SC研究中缺乏这些分析方法的全面时间表和全球影响评估.
研究的目的:
- 对SysBio和AI对SC研究和治疗开发的贡献进行范围审查.
- 分析从2000年到2024年在SC研究中SysBio和AI应用的时间表和全球分布.
主要方法:
- 在2000年至2024年期间在PubMed上发表的文献范围审查.
- 分析的重点是与SysBio,AI,ML,DL和SC研究相关的出版物.
主要成果:
- 在2000年至2021年期间,在SC研究中观察到SysBio和AI的研究成果增加了8到10倍,自2010年以来,AI生产增加了10倍.
- SysBio和AI主要用于临床前基础研究,自2010年以来,在翻译医学中越来越多的应用.
- 美国和英国领导全球SysBio和AI研究SCs,AI的突出地位从胚胎干细胞 (ESCs) 转向诱导多能干细胞 (iPSCs) 和介质干细胞 (MSCs).
结论:
- 在过去的二十年中,人工智能,SysBio和SC研究在全球范围内发生了显著的演变和日益趋同.
- 这三个领域都经历了实质性的增长,人工智能研究在过去十年中显示出指数级增长.
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