在临床前研究中使用人工智能驱动的虚拟细胞模型:技术途径,验证机制和临床翻译潜力
Chunyu Ma1,2, Han Zhang2, Yiwei Rao2
1Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China.
NPJ digital medicine
|December 11, 2025
概括
由人工智能驱动的虚拟细胞模型整合了omics数据,用于生命科学中的精确预测. 这些先进的计算工具加速了药物发现和疾病建模,为精准医学铺平了道路.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 人工智能驱动的虚拟细胞模型正在成为生命科学研究中的强大工具.
- 这些模型整合了多式联通的奥米克数据,包括单细胞转录组和蛋白组.
- 像深度生成模型和图形神经网络这样的先进算法是关键组件.
研究的目的:
- 审查人工智能驱动的虚拟细胞模型的技术途径和验证机制.
- 突出虚拟细胞在个性化药物查和疾病建模中的应用.
- 讨论临床翻译和药物发现的挑战和未来方向.
主要方法:
- 整合多式联通数据 (例如,单细胞转录组学,蛋白质组学).
- 应用先进的算法,如深度生成模型和图形神经网络.
- 通过闭环工作流程进行验证,包括计算评估和实验验证 (CRISPR测试,有机体平台).
主要成果:
- 虚拟细胞能够高精度预测药物反应,基因干扰和疾病进展.
- 证明了减少动物试验和优化治疗策略的潜力.
- 在个性化药物查和复杂的疾病建模中确定了应用.
结论:
- 人工智能驱动的虚拟细胞为生命科学和精密医学提供了变革性的潜力.
- 解决监管接受,数据隐私和模型解释性方面的挑战对于广泛采用至关重要.
- 跨学科的整合和标准化是推动虚拟细胞技术在药物发现中的关键.
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