scCont: Interpretable Contrastive Learning for Functional Characterization of Cellular State Transitions
Neal Kewalramani, Stefan Wuchty, Mark Crovella, Indranil Paul, Andrew Emili
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.
bioRxiv · Nov 6, 2026 · PreprintBioinformatics