RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
RNA-FM:基于WSI的全基因组RNA-Seq预测的流匹配生成模型
机构 * School of Computer Science, The University of Sydney, Australia(悉尼大学计算机科学学院) ; Engineering Division, Lawrence Berkeley National Lab, USA(伯克利国家实验室工程部) ; Berkeley Biomedical Data Science Center, Lawrence Berkeley National Lab, USA(伯克利生物医学数据科学中心) ; Department of Computer Science, University of Maryland College Park, USA(马里兰大学学院市计算机科学系)
AI总结 本文提出RNA-FM模型,通过流匹配生成框架实现基于WSI的全基因组RNA-Seq预测,利用连续时间条件传输问题学习速度场,实现基因表达分布的映射,提升预测的可扩展性和生物解释性。
Comments 15 pages, 13 tables, 3 figures. Accepted by the Forty-Third International Conference on Machine Learning (ICML2026). Code is available at https://github.com/YXSong000/RNA-FM