Abstract. We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete a...
The lack of precise numerical information for the values of biological parameters severely limits the development and analysis of models of genetic regulatory networks. To deal wit...
Switching activity estimation is a crucial step in estimating dynamic power consumption in CMOS circuits. In [1], we proposed a new switching probability model based on Bayesian N...
In this paper we survey work being conducted at Imperial College on the use of machine learning to build Systems Biology models of the effects of toxins on biochemical pathways. Se...
Abstract-- An inverse modeling problem for systems governed by first-order, hyperbolic partial differential equations subject to periodic forcing is investigated. The problem is de...
Qingfang Wu, Mohammad Rafiee, Andrew Tinka, Alexan...