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

Martin Paluszewski,Thomas Hamelryck

doi:10.1186/1471-2105-11-126

Martin Paluszewski, Thomas Hamelryck

Open Access

https://doi.org/10.1186/1471-2105-11-126

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Journal: BMC bioinformatics	Publication Date: Mar 12, 2010
Citations: 41	License type: CC BY 2.0

Affiliation: University of Copenhagen

Abstract

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

Highlights

Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs)
DBNs have been applied with great success to a large number of problems in various fields
Mocapy++ has a number of attractive features that are not found together in other toolkits [14]: it is open source, implemented in C++ for optimal speed efficiency and supports directional statistics

Summary

Conclusions

Mocapy++ has a number of attractive features that are not found together in other toolkits [14]: it is open source, implemented in C++ for optimal speed efficiency and supports directional statistics This branch of statistics deals with data on unusual manifolds such as the sphere or the torus [25], which is useful to formulate probabilistic models of biomolecular structure in atomic detail [9,10,11,12]. The use of S-EM for parameter estimation avoids problems with convergence [16,17] and allows for the use of large datasets, which are Figure 5 Log-likelihood evolution during S-EM training.

Background

Results and Discussion

Bishop CM

16. Nielsen SF

24. Kent JT