Hang Li

Inferring gene regulatory networks from single-cell RNA sequencing has long split into two routes: graph methods train on the whole network as one graph and tend to overfit, while pairwise methods split it into transcription factor–target pairs, gaining abundant samples but losing global topology. scGPL uses a dual-branch design to model both the global regulatory graph and a local subgraph around each candidate pair, reaching the best performance on seven benchmark datasets, with AUPRC up to 13.7% above the strongest baseline.

Inferring gene regulatory networks from single-cell RNA sequencing has long split into two routes: graph methods train on the whole network as one graph and tend to overfit, while pairwise methods split it into transcription factor–target pairs, gaining abundant samples but losing global topology. scGPL uses a dual-branch design to model both the global regulatory graph and a local subgraph around each candidate pair, reaching the best performance on seven benchmark datasets, with AUPRC up to 13.7% above the strongest baseline.