The development of structure-learning algorithms for gene regulatory networks depends heavily on the availability of synthetic data sets that contain both the original network and ...
Koenraad Van Leemput, Tim Van den Bulcke, Thomas D...
Background: Reverse-engineering regulatory networks is one of the central challenges for computational biology. Many techniques have been developed to accomplish this by utilizing...
Shawn Cokus, Sherri Rose, David Haynor, Niels Gr&o...
Abstract. In this study we propose a novel model for the representation of biological networks and provide algorithms for learning model parameters from experimental data. Our appr...
Background: Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DN...
Abstract. We report on the results of an investigation into the integration of Petri nets and ordinary differential equations (ODEs) for the modelling and analysis of biochemical n...