Qingchao Jiang

PRIME: scalable, robust inference of mechanistic cell states from multimodal single-cell counts via probability generating functions

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.

Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions

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.

Neural network aided approximation and parameterinference of stochastic models of gene expression

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