Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
Background: Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) datab...
Background: In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centralit...
Kang Ning, Hoong Kee Ng, Sriganesh Srihari, Hon Wa...
The usefulness of parameterized algorithmics has often depended on what Niedermeier has called, "the art of problem parameterization." In this paper we introduce and expl...
Michael R. Fellows, Serge Gaspers, Frances A. Rosa...
Background: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing ...
Brandon W. Higgs, Jennifer W. Weller, Jeffrey L. S...