研究人员正在开发新的AI模型来预测细胞动力学和对扰动的反应。一种方法Chreode使用细胞世界模型进行一步时间预测，并在扰动预测的基因状态嵌入方面显示出改进。另一项研究探索了时间图学习，其中细胞状态被建模为演化的图结构，在预测生物状态方面优于现有的基础模型。此外，一个潜在的动力学因果生成模型CITE-VAE旨在捕捉潜在的细胞程序及其扰动驱动的动力学，展示了对未见扰动的改进泛化能力。 AI
影响 这些用于生物系统的AI模型的进步可以通过实现更准确的细胞反应的计算机内预测来加速药物发现和个性化医疗。
排序理由 多篇arXiv上发表的研究论文详细介绍了用于生物系统预测的新颖AI模型。
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
arXiv:2605.28111v1 Announce Type: new Abstract: Predicting how a cell will change its transcriptional state under a developmental signal or a genetic perturbation is the computational core of in-silico biology and the AI Virtual Cell program. Existing approaches either fit static…
arXiv:2605.28659v1 Announce Type: new Abstract: Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operate in static …
Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operate in static settings and do not explicitly model the tempora…
arXiv:2605.25581v1 Announce Type: new Abstract: Single-cell perturbation prediction aims to infer how cells respond to unseen interventions and to achieve out-of-distribution (OOD) generalization, providing a computational route to understanding how perturbations reshape cellular…