Z. Kilic

This paper introduces Bayes-smRD, a method to analyze single-molecule reaction-diffusion using photon arrival data, revealing molecule-specific properties with reduced data and photodamage.

BNP-Track is a framework for superresolved tracking of fluorescent molecules that uses Bayesian nonparametrics to infer emitter numbers and tracks, outperforming conventional methods in crowded environments.

The paper introduces a Bayesian nonparametric method to infer gene networks from single-cell RNA data by simultaneously learning gene states, connectivities, and rate parameters.

This paper develops a continuous-time hidden Markov jump process to learn kinetic rates from smFRET data beyond acquisition rates, validated on simulated and experimental data.

This paper generalizes hidden Markov models to continuous time for analyzing single molecule kinetics, introducing hidden Markov jump processes to handle fast transitions without discrete time assumptions.

This paper introduces a Bayesian sticky HMM approach to analyze RNA polymerase residence times without distinguishing between long and short events, revealing force-dependent dynamics and limited effects of GreB.