Huai-Yu Liou

Across several large Perturb-seq datasets, a generative framework called Graph-guided Flow Matching (GFM) combines three biological knowledge graphs, covering Gene Ontology, protein interactions and perturbation similarity, with latent flow matching to predict single-cell transcriptomes after unseen perturbations. It outperforms a linear baseline and the deep-learning models CellFlow, GEARS and scGPT on unseen single-gene perturbations, trains up to 30 times faster, and keeps its advantage as training data shrink; on two-gene perturbations it does better when neither gene was seen in training.

Across several large Perturb-seq datasets, a generative framework called Graph-guided Flow Matching (GFM) combines three biological knowledge graphs, covering Gene Ontology, protein interactions and perturbation similarity, with latent flow matching to predict single-cell transcriptomes after unseen perturbations. It outperforms a linear baseline and the deep-learning models CellFlow, GEARS and scGPT on unseen single-gene perturbations, trains up to 30 times faster, and keeps its advantage as training data shrink; on two-gene perturbations it does better when neither gene was seen in training.