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人工智能和系统生物学分析在干细胞研究和治疗开发中的应用

Thayna Silva-Sousa^1,2,3,4, Júlia Nakanishi Usuda^1,2,3,4,5,6, Nada Al-Arawe^{1,2,3,4,5,7,8}

¹BIH Center for Regenerative Therapies (BCRT), Charité Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health (BIH), Berlin 10117, Germany.

Stem cells translational medicine

|September 28, 2025

概括

系统生物学 (SysBio) 和人工智能 (AI) 可以加速干细胞治疗的翻译. 集成SysBio和AI分析多omics数据优化临床试验,改善患者的治疗结果和治疗开发.

科学领域:

再生医学是一种再生医学.
生物技术是生物技术.
计算生物学计算生物学

背景情况:

干细胞研究已取得重大进展,但临床转化仍然缓慢.
来自产品开发和临床试验的数据利用效率低下,阻碍了干细胞治疗的实施.
需要解决干细胞疗法的有效临床转化的主要障碍.

研究的目的:

探索系统生物学 (SysBio) 和人工智能 (AI) 在克服干细胞治疗翻译障碍方面的作用.
突出SysBio和AI如何提高对产品和患者表现的理解.
引入由SysBioAI.AI支持的"精细临床翻译的代循环"概念.

主要方法:

总结SysBio和AI在干细胞研究和治疗开发中的贡献.
利用细胞产品分析,临床试验设计和患者监测方面的进步.
使用SysBioAI.使用患者和临床试验结果分析大规模的多omics数据.

主要成果:

SysBioAI 能够快速,综合分析复杂的生物数据.
"精细临床翻译的代循环"概念促进了适应性发展周期.
通过生物标志物识别,SysBioAI支持以患者为中心的安全性和有效性评估.

结论:

集成的SysBioAI应用程序优化了临床试验设计和结果.
通过识别患者特异性反应,SysBioAI提高了治疗的安全性和有效性.
这种方法激发了以患者为中心,适应性强的下一代深度医学策略.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.介酶体干细胞/干细胞干细胞研究研究干细胞研究.系统生物学系统生物学

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