From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
Researchers have developed a new framework called single-cell Flow Matching (scFM) to better model the dynamics of gene expression in single cells. This method addresses challenges in existing techniques, such as ambiguity in transitions between discrete time points and error accumulation during long-term predictions. By using conditional flow matching and bidirectional velocity fields, scFM improves the accuracy of temporal interpolation and extrapolation, leading to more faithful reconstructions of gene expression dynamics. AI
IMPACT Introduces a novel generative framework for analyzing complex biological time-series data, potentially improving drug discovery and disease research.