Yiling Wang

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.

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.