4 papers
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…
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…
Kolmogorov-Arnold Network for Gene Regulatory Network Inference
Tsz Pan Tong, Aoran Wang, George Panagopoulos +1
Gene regulation is central to understanding cellular processes and development, potentially leading to the discovery of new treatments for diseases and personalized medicine. Infer…
Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data
Tsz Pan Tong, Aoran Wang, George Panagopoulos +1
We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generat…