%0 Journal Article %1 Klarner2017PyBoolNet %A Klarner, Hannes %A Streck, Adam %A Siebert, Heike %D 2017 %J Bioinformatics (Oxford, England) %K boolean-networks software %N 5 %P 770--772 %T PyBoolNet: a python package for the generation, analysis and visualization of boolean networks. %U http://view.ncbi.nlm.nih.gov/pubmed/27797783 %V 33 %X The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts. P y B ool N et is a Python package for working with Boolean networks that supports simple access to model checking via N u SMV, standard graph algorithms via N etwork X and visualization via dot . In addition, state of the art attractor computation exploiting P otassco ASP is implemented. The package is function-based and uses only native Python and N etwork X data types. https://github.com/hklarner/PyBoolNet. hannes.klarner@fu-berlin.de.

@article{Klarner2017PyBoolNet, abstract = {The goal of this project is to provide a simple interface to working with Boolean networks. Emphasis is put on easy access to a large number of common tasks including the generation and manipulation of networks, attractor and basin computation, model checking and trap space computation, execution of established graph algorithms as well as graph drawing and layouts. P y B ool N et is a Python package for working with Boolean networks that supports simple access to model checking via N u {SMV}, standard graph algorithms via N etwork X and visualization via dot . In addition, state of the art attractor computation exploiting P otassco {ASP} is implemented. The package is function-based and uses only native Python and N etwork X data types. {https://github.com/hklarner/PyBoolNet}. hannes.klarner@fu-berlin.de.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Klarner, Hannes and Streck, Adam and Siebert, Heike}, biburl = {https://www.bibsonomy.org/bibtex/24f9d066da04adba78a376e089dc5182b/karthikraman}, citeulike-article-id = {14401957}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/27797783}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=27797783}, day = 01, interhash = {5776db6b94320e01e9e14b02b34d1258}, intrahash = {4f9d066da04adba78a376e089dc5182b}, issn = {1367-4811}, journal = {Bioinformatics (Oxford, England)}, keywords = {boolean-networks software}, month = mar, number = 5, pages = {770--772}, pmid = {27797783}, posted-at = {2017-07-29 15:17:35}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {{PyBoolNet}: a python package for the generation, analysis and visualization of boolean networks.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/27797783}, volume = 33, year = 2017 }