A key challenge in inferring the structure and dynamics of gene regulatory networks is the
representation of various sources of stochasticity in mathematical models. In this work, we …

We consider the problem of estimating a gene regulatory network under switching noise
dynamics. Several stochastic models based on discretized Langevin dynamics are …

This dissertation centers on the development of Bayesian methods for learning different
types of variation in switching nonlinear gene regulatory networks (GRNs). A new nonlinear …

In this paper, we propose a novel ensemble Kalman filter based particle filter for gene
regulatory networks (GRNs) analysis, which incorporates ensemble Kalman filter into …

Correct and accurate analysis of the similarity score using noisy time series gene expression
data plays important role in understanding gene level biological systems. In this study, we …