{"messages":[{"status":"ok","category":"all"}], "collection":[{"title":"FateDriver: Weakly supervised dual-view graph learning for transition-specific prioritization of candidate regulators in cellular reprogramming","authors":"Sung, J.; Son, K.; Cha, H.-J.; Kim, W.; Lee, H.","author_corresponding":"Wankyu Kim","author_corresponding_institution":"Ewha Womans University","doi":"10.64898\/2026.10.27.762150","date":"2026-11-01","version":"1","type":"new results","license":"cc_by_nc_nd","category":"bioinformatics","jatsxml":"https:\/\/www.biorxiv.org\/content\/early\/2026\/11\/01\/2026.10.27.762150.source.xml","abstract":"Cellular reprogramming supports regenerative medicine, yet the endogenous regulators of a specified source-to-target transition remain incompletely defined. Existing computational methods rank candidate regulators from network topology, trajectory dynamics, or cell-state labels, leaving unused the factors experimentally delivered to induce the transition. We present FateDriver, a weakly supervised dual-view graph neural network that jointly represents cells and genes from single-cell transcriptomes, treats the delivered factors as sparse gene-level labels, and ranks the remaining genes for a specified transition under a positive--unlabeled formulation. Across two OSKM datasets and one GMT dataset, FateDriver ranked first or second among six methods in recovering externally curated reference genes, exceeded a supervision-matched non-graph model despite weaker co-expression with the delivered factors, and achieved the highest recovery of protein targets of pluripotency-inducing chemical cocktails in both OSKM datasets. Holding either the source or the endpoint fixed yielded distinct candidate programs, including a DDX58--interferon axis associated with cells lacking endogenous pluripotency markers. Applying the same framework to candidate source--target pairs prioritized distinct candidate origins for two in vivo partially reprogrammed intestinal populations. FateDriver converts sparse experimental knowledge into transition-specific hypotheses for regulator prioritization from single-cell trajectories.","published":"NA","server":"bioRxiv"}]}