使用扩散模型预测细胞发育和对干扰的反应
bioRxiv : the preprint server for biology
|September 5, 2025
概括
它可以预测细胞的转录变化,加速药物发现和疾病机制的理解. 这种计算工具有助于快速生成精准医学应用的假设.
科学领域:
- 计算生物学
- 基因组学
- 系统生物学
背景情况:
- 单细胞测序揭示了细胞异质性,但将转录组变化映射到刺激是具有挑战性的.
- 目前研究细胞对辐射或药物等刺激的反应的实验方法是劳动密集的.
- 阐明疾病机制需要有效的工具来预测各种条件下的细胞行为.
研究的目的:
- 开发一种新的计算框架,Squidiff,用于预测各种细胞类型的转录变化,以应对环境刺激.
- 证明 Squidiff 在模拟复杂的生物过程和预测细胞反应中的实用性.
- 促进分子景观的选以加速假设生成和药物发现.
主要方法:
- 开发了基于扩散模型的生成框架Squidiff,用于预测转录组变化.
- 集成的连续无声化和语义特征学习以捕捉短暂的细胞状态.
- 将模型应用于包括细胞分化,基因扰动和药物反应预测在内的各种场景.
主要成果:
- 在各种细胞类型和条件中准确预测转录基因景观.
- 在模拟细胞分化,基因扰动和药物反应方面表现出强度.
- 成功模拟了血管器官发育和细胞对中子辐射和生长因子的反应.
结论:
- 它可以有效地预测转录组变化,克服实验的局限性.
- 该框架有助于快速生成假设,并为精准医学提供见解.
- 在生物研究和药物开发的计算工具中,
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