Mocapy plus plus

Mocapy plus plus implements parameter learning and inference for dynamic Bayesian networks (DBNs) in C++, enabling probabilistic modeling of biomolecular angles, directions, and orientations.

Key Features:

• Implementation: Implemented in C++ for computational parameter learning and inference in DBNs.
• DBN architectures: Supports various dynamic Bayesian network architectures for modeling temporal and sequential dependencies.
• Directional statistics: Supports directional distributions including the Kent distribution on the sphere and the bivariate von Mises distribution on the torus for angles, directions, and orientations.
• Supported distributions: Accommodates discrete, multinomial, Gaussian, Kent, von Mises, and Poisson nodes.
• Algorithms: Performs inference and parameter estimation using Gibbs sampling and Stochastic Expectation-Maximization (Stochastic-EM).
• Biomolecular modeling: Facilitates probabilistic models of protein and RNA structures at an atomic level using specialized distributions.

Scientific Applications:

• Protein structure modeling: Formulates probabilistic models of protein structures at atomic resolution using directional and standard distributions.
• RNA structure modeling: Formulates probabilistic models of RNA structures at atomic resolution using directional and standard distributions.
• Angular and directional analysis: Models and analyzes angular, directional, and orientational data via Kent and bivariate von Mises distributions.
• Dynamic probabilistic modeling: Models temporal and sequential biological processes using dynamic Bayesian networks.

Methodology:

Parameter learning and inference are performed on DBNs implemented in C++ using Gibbs sampling and Stochastic Expectation-Maximization (Stochastic-EM), with nodes supporting discrete, multinomial, Gaussian, Kent, von Mises, and Poisson distributions.