%0 Journal Article
%T Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
%A Martin Paluszewski
%A Thomas Hamelryck
%J BMC Bioinformatics
%D 2010
%I BioMed Central
%R 10.1186/1471-2105-11-126
%X 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.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.A Bayesian network (BN) represents a set of variables and their joint probability distribution using a directed acyclic graph [1,2]. A dynamic Bayesian network (DBN) is a BN that represents sequences, such as time-series from speech data or biological sequences [3]. One of the simplest examples of a DBN is the well known hidden Markov model (HMM) [4,5]. DBNs have been applied with great success to a large number of problems in various fields. In bioinformatics, DBNs are especially relevant because of the sequential nature of biological molecules, and have therefore proven suitable for tackling a large number of problems. Examples are protein homologue detection [6], protein secondary structure prediction [7,8], gene finding [5], multiple sequence alignment [5] and sampling of protein conformations [9,10].Here, we present a general, open source toolkit, called Mocapy++, for inference and learning in BNs and especially DBNs. The main purpose of Mocapy++ is to allow the user to concentrate on the probabilistic model itself, without having to implement customized algorithms. The name Mocapy stands for Markov chain Monte Carlo and Python: the key ingredients in the original implementation of Mocapy (T. Hamelryck, University of Copenhagen, 2004, unpublished). Today, Mocapy has been re-implemented in C++ but the name is kept for historical reason
%U http://www.biomedcentral.com/1471-2105/11/126