DualCPT: Dual-branch Modeling for Cellular Phenotype Transition

Cell phenotype transition refers to the changes in the morphology, function, and surface markers of cells that occur under specific environmental conditions or physiological states, based on their genomic information and external signals. This process plays an important role in development, tissue repair, and responses to external stimuli such as infection or inflammation. Traditional bioinformatics methods for addressing cell type transition often rely on hypothesis-driven models, which may not fully capture the complexity and heterogeneity of the transition processes. In this paper, we introduce DualCPT, a cell phenotype transition and differentiation model based on Markov processes. Specifically, the model consists of a classification branch and a transition branch. The transition branch identifies regulatory genes involved in cell phenotype transition and differentiation. In the classification branch, we evaluate the model’s overall performance on general cell type classification tasks using a comprehensive multi-metric evaluation framework; in the transition branch, we implement a token pruning-based approach for critical locus discovery and enhance information interaction between full-sequence contexts and prioritized regulatory sites via an improved multi-head attention mechanism. Cell phenotype transition tasks are further assessed by uncertainty quantification and confidence calibration. In particular, in gene knockout experiments, we found that knocking out important genes alters the probability of cell phenotype transition and differentiation, and knocking out a certain number of essential genes can terminate specific transition processes. Data, code, and checkpoints are publicly available at https://github.com/Ssupercoder/DualCPT.