BoolNet is an R package for generation, reconstruction, simulation and analysis of synchronous, asynchronous and probabilistic Boolean networks. This page provides help on the installation and usage of the package. The most recent version of the package and its documentation is available from BoolNet's CRAN page.
Documentation is included in the R package. You can get help for a command by typing ?<command> (e.g. ?getAttractors) inside R when the package is loaded.
Additionally, a step-by-step tutorial is available here.
A description of methods and algorithms can be accessed here. Furthermore, this paper discusses the attractor search in BoolNet.
The most recent source and binary packages for BoolNet are available from CRAN. The Debian Med team also maintains a package r-cran-boolnet which can be obtained from the official repositories for Debian and Ubuntu.
In an R console, type install.packages("BoolNet"). The package is now installed and can be loaded via library(BoolNet). The packages igraph and XML are installed automatically, as BoolNet depends on them.
To install BoolNet from the official Debian/Ubuntu repositories, open a terminal and type
sudo apt-get install r-cran-boolnet
Please note that the Debian package may not always be the most recent version of BoolNet.
To install BoolNet manually, the sources (BoolNet_<version>.tar.gz) must be downloaded from CRAN and compiled. As a prerequisite, the packages igraph and XML must be installed.
To cite the package in publications, use:
Müssel C, Hopfensitz M, Kestler HA. BoolNet - an R package for generation, reconstruction, and analysis of Boolean networks. Bioinformatics 26(10): 1378-1380, 2010. https://doi.org/10.1093/bioinformatics/btq124
Wissenschaftlicher Mitarbeiter (m/w/d)
Our paper "A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective" has been published in npj systems biology and applications.
"Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician" has been published in PLoS One.
"Self-Assessment of Having COVID-19 With the Corona Check Mhealth App" has been published in IEEE Journal of Biomedical and Health Informatics.
Our first quantum computing paper "Leveraging quantum computing for dynamic analyses of logical networks in systems biology" has been published in Patterns.
Our paper "Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images" has been published in Frontiers in Artificial Intelligence.
"Vaccine Side Effects in Health Care Workers after Vaccination against SARS-CoV-2: Data from TüSeRe:exact Study" has been published in Viruses-Basel.
"PREDICT-juvenile-stroke: PRospective evaluation of a prediction score determining individual clinical outcome three months after ischemic stroke in young adults – a study protocol" has been published in BMC Neurology.
Our paper "Federated Electronic Data Capture (fEDC): Architecture and Prototype" has been accepted for publiaction in the Journal of Biomedical Informatics.
Our paper "Efficient cross-valdation traversals in feature subset selection" has been published in Scientific Reports.
Our paper "CANTATA - prediction of missing links in Boolean networks using genetic programming" has been published in Bioinformatics.
Our paper "Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives" has been published in the Journal of Medical Internet Research mhealth and uhealth.
Our paper "Identification of dynamic driver sets controlling phenotypical landscapes" has been published in the Computational and Structural Biotechnology Journal.