AI RESEARCH
Applications of temporal graph learning for predicting the dynamics of biological systems
arXiv CS.LG
•
ArXi:2605.28659v1 Announce Type: new 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 temporal evolution of developmental programs in the cell. Modeling such dynamics is important for understanding how cellular states progressively emerge, differentiate, and reorganize during development or disease progression.