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cs.CE2026
FlowGRN+: Improving Gene Regulatory Network Inference by Spline Fitting and Manifold Projection in Conditional Flow Matching (Technical Report)
Tsz Pan Tong, Jun Pang
Gene regulatory networks (GRNs) are fundamental in understanding cellular dynamics and underlying mechanisms during development and disease. Although scRNA-seq technologies have en…
cs.CE2026
FlowGRN: Scalable and Dropout-Robust Gene Regulatory Network Inference via Flow Matching-Based Trajectory Reconstruction (Technical Report)
Tsz Pan Tong, Jun Pang
Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data offers insights into cellular behavior, but is complicated by the lack of temporal inform…