Akshata Hegde

GRNPred is a graph transformer model pretrained on gene expression data to infer gene regulatory networks, achieving high AUROC and AUPRC scores on benchmark datasets.

GRNFormer infers gene regulatory networks from transcriptomics data using a graph transformer framework, outperforming existing methods and recovering known and novel regulatory interactions.

The paper uses multi-omics data and machine learning to analyze oviductal responses to gametes and embryos in mice, identifying region-specific transcriptional and proteomic changes.