Background: With the popularisation of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently...
Background: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome...
Bahrad A. Sokhansanj, J. Patrick Fitch, Judy N. Qu...
Abstract. We describe a probabilistic model, implemented as a dynamic Bayesian network, that can be used to predict nucleosome positioning along a chromosome based on one or more g...
Sheila M. Reynolds, Zhiping Weng, Jeff A. Bilmes, ...
Background: Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of function...
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...