单细胞体质学的变革性进步:对基础模型,多式联络整合和计算生态系统的全面审查
Taylor Yiu1,2, Bin Chen1, Haoyu Wang1
1University of Shanghai for Science and Technology, Shanghai, 200093, China.
Journal of translational medicine
|October 28, 2025
概括
基金会的模型正在彻底改变单细胞多组体分析,以了解细胞异质性和疾病的洞察力. 这些人工智能方法增强了数据集成和计算生物学,为精准医学铺平了道路.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞多omics技术提供高分辨率的细胞分析.
- 来自自然语言处理的基础模型正在适应复杂的生物数据.
研究的目的:
- 审查单细胞多组和基础模型应用的最新进展.
- 提出将人工智能与生物数据集成的战略,以获得机械洞察力和精确医学.
主要方法:
- 应用基础模型 (例如 scGPT, scPlantFormer) 进行细胞注释和基因调控网络推断.
- 采用诸如病理调整嵌入和张量融合等技术的多式数据集成.
- 利用联合计算平台进行分散和可重复的分析.
主要成果:
- 基础模型在跨物种细胞注释和in silico扰动建模方面取得了成功.
- 多模式集成协调不同类型的数据 (转录基因组,表观基因组,蛋白质基因组,空间基因组) 以映射监管网络.
- 联合平台使协作和标准化数据分析成为可能.
结论:
- 人工智能,特别是基础模型,正在改变单细胞多omics数据分析.
- 挑战包括技术变化,模型解释性和临床翻译.
- 标准化,多式联络知识图和人工智能与人类的合作对于机械生物学和精密医学的未来进步至关重要.
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