Starting from the logical description of gene regulatory networks developed by R. Thomas, we introduce an enhanced modelling approach based on timed automata. We obtain a refined ...
Background: Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differen...
Bruno M. Tesson, Rainer Breitling, Ritsert C. Jans...
We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial ...
In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe ...
Abstract--Glass models are frequently used to model gene regulatory networks. A distinct feature of the Glass model is that its dynamics can be formalized as paths through multi-di...