Kuan-Chiun Tung

JuloVelo moves gene kinetics pattern identification ahead of neural network training: each gene is first classified as Circle, Complete induction, or Complete repression from its unspliced–spliced RNA relationship, and training starts from pattern-matched pretrained weights, steering cell-specific kinetics away from biologically implausible parameter regimes. Across eight biological datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, plus simulated data with known kinetics, it was more stable than constant-rate and cell-specific models, was the only method to resolve all five hippocampal neurogenesis branches, and used promoter chromatin accessibility to process multiome data.

JuloVelo moves gene kinetics pattern identification ahead of neural network training: each gene is first classified as Circle, Complete induction, or Complete repression from its unspliced–spliced RNA relationship, and training starts from pattern-matched pretrained weights, steering cell-specific kinetics away from biologically implausible parameter regimes. Across eight biological datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, plus simulated data with known kinetics, it was more stable than constant-rate and cell-specific models, was the only method to resolve all five hippocampal neurogenesis branches, and used promoter chromatin accessibility to process multiome data.