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Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks

Martin Paluszewski* and Thomas Hamelryck

Author Affiliations

Bioinformatics Centre, University of Copenhagen, Denmark

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BMC Bioinformatics 2010, 11:126  doi:10.1186/1471-2105-11-126

Published: 12 March 2010

Abstract

Background

Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).

Results

The program package is freely available under the GNU General Public Licence (GPL) from SourceForge http://sourceforge.net/projects/mocapy webcite. The package contains the source for building the Mocapy++ library, several usage examples and the user manual.

Conclusions

Mocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.