Indranil Paul

scCont is an unsupervised contrastive learning framework that extracts interpretable functional transition units from scRNA-seq data, outperforming baseline methods in capturing biologically meaningful dynamics.

DESP resolves cell state contributions to bulk molecular measurements using single-cell data, demonstrating accuracy in reconstructing proteome and transcriptome signatures during epithelial-to-mesenchymal transition.

The paper maps EMT transitions through multi-omic profiling, identifies key regulatory factors, and reveals discordance between molecular layers in human mammary epithelial cells.

This paper demonstrates that pancreatic tumor organoids (PDOs) recapitulate PDX models' features and can identify clinically-actionable biomarkers in blood.