The complexity of gene regulatory network models stems from the fact that the models should be able to represent continuous, discrete as well as stochastic aspects of gene regulati...
: Recently, many approaches to model regulatory networks have been proposed in the systems biology domain, however the task is far from being solved. In this paper we propose an an...
Timur Fayruzov, Jeroen Janssen, Dirk Vermeir, Chri...
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
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...
Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reas...
Yuji Zhang, Jianhua Xuan, Benildo de los Reyes, Ro...