Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing hig...
This paper describes the application of a suite of innovative information processing and analytic support tools developed at The MITRE Corporation, in some cases as extensions of ...
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Background: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomic...
Markus Brameier, Josien Haan, Andrea Krings, Rober...
We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...