%0 Journal Article %1 Harrigan2017MSMBuilder %A Harrigan, Matthew P. %A Sultan, Mohammad M. %A Hernández, Carlos X. %A Husic, Brooke E. %A Eastman, Peter %A Schwantes, Christian R. %A Beauchamp, Kyle A. %A McGibbon, Robert T. %A Pande, Vijay S. %D 2017 %J Biophysical Journal %K MSMs markov-state-models molecular-dynamics msmbuilder %N 1 %P 10 - 15 %R https://doi.org/10.1016/j.bpj.2016.10.042 %T MSMBuilder: Statistical Models for Biomolecular Dynamics %U http://www.sciencedirect.com/science/article/pii/S0006349516310487 %V 112 %X Abstract \MSMBuilder\ is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. \MSMBuilder\ is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer \MSM\ methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. \MSMBuilder\ boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. \MSMBuilder\ was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.

@article{Harrigan2017MSMBuilder, abstract = {Abstract \{MSMBuilder\} is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. \{MSMBuilder\} is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer \{MSM\} methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. \{MSMBuilder\} boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. \{MSMBuilder\} was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements. }, added-at = {2017-05-17T18:29:29.000+0200}, author = {Harrigan, Matthew P. and Sultan, Mohammad M. and Hernández, Carlos X. and Husic, Brooke E. and Eastman, Peter and Schwantes, Christian R. and Beauchamp, Kyle A. and McGibbon, Robert T. and Pande, Vijay S.}, biburl = {https://www.bibsonomy.org/bibtex/25dd3958d41d37f60a40e71c2f02ec2da/salotz}, doi = {https://doi.org/10.1016/j.bpj.2016.10.042}, interhash = {1d280fbd53b1ca1040106333ca7aed13}, intrahash = {5dd3958d41d37f60a40e71c2f02ec2da}, issn = {0006-3495}, journal = {Biophysical Journal }, keywords = {MSMs markov-state-models molecular-dynamics msmbuilder}, number = 1, pages = {10 - 15}, timestamp = {2017-05-17T18:29:29.000+0200}, title = {MSMBuilder: Statistical Models for Biomolecular Dynamics }, url = {http://www.sciencedirect.com/science/article/pii/S0006349516310487}, volume = 112, year = 2017 }