Zhixing Cao

This paper introduces an uncertainty-aware Bayesian information criterion for selecting stochastic gene-expression models from single-cell counts, reducing incorrect selection of more complex models.

This paper develops a likelihood-based framework using probability generating functions for parameter inference and model selection in stochastic gene expression from single-cell counts.

PRIME infers mechanistic cell states from multimodal single-cell data by embedding counts in a probability generating function space, enabling robust kinetic analysis and clustering.

This paper introduces a probability generating function-based method for parameter inference in stochastic biochemical reactions and shows it outperforms existing methods in efficiency, accuracy, and robustness.

This paper derives an analytical solution for stochastic gene expression dynamics with mRNA-sRNA interactions and develops a parameter-inference method for efficient kinetic parameter estimation.

The paper derives an exact steady-state solution for the joint distribution of nuclear and cytoplasmic mRNA levels and uses it to quantify bursty gene expression in human cells.

This paper evaluates Negative Binomial models and parameter estimation in scRNA-seq data, finding that NB fits occur in diverse regimes and that burst parameters are reliably informative.

This paper develops a graph neural network method to predict spatio-temporal stochastic gene expression efficiently, achieving faster computation and extrapolation capabilities.

DelaySSAToolkit.jl provides a Julia package for simulating reaction systems with time delays using exact formulations of the delay stochastic simulation algorithm.

This paper uses neural networks to approximate non-Markov stochastic gene expression models and infer their parameters from noisy data.

The paper presents a stochastic model of gene expression incorporating polymerase recruitment and pause release, showing it explains experimental data better than the telegraph model and predicts gene dosage compensation.