Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used
for the study of the topology and dynamic aspects of biological systems. The combined use …

Much of our current knowledge of biology has been constructed based on population-
average measurements. However, advances in single-cell analysis have demonstrated the …

This paper addresses the observability of a switched Boolean control network (SBCN) using
semi-tensor product (STP) of matrices. First, the observability of the SBCN is determined …

The present paper investigates the global stability and stabilization of switched Boolean
network with impulsive effects. By using the semi-tensor product, the algebraic form of the …

This paper studies deep neural networks for solving extremely large linear systems arising
from high-dimensional problems. Because of the curse of dimensionality, it is expensive to …

In this manuscript we propose and implement a dimension reduction algorithm of AND-NOT
networks for the purpose of steady state computation. Our method of network reduction …

The improved in vitro regulation of human embryonic stem cell (hESC) pluripotency and
differentiation trajectories is required for their promising clinical applications. The temporal …

Abstract Background Solutions to stochastic Boolean models are usually estimated by
Monte Carlo simulations, but as the state space of these models can be enormous, there is …

In this paper, we study deep neural networks for solving extremely large linear systems
arising from physical relevant problems. Because of the curse of dimensionality, it is …

Multivariate Markov chain models have previously been proposed in for studying dependent
multiple categorical data sequences. For a given multivariate Markov chain model, an …