Title
MaBoSS 2.0: an environment for stochastic Boolean modeling.
Abstract
Motivation: Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model. Results: We present a new version of MaBoSS 2.0), including an updated version of the core software and an environment. With this environment, the needs for modeling signaling pathways are facilitated, including model construction, visualization, simulations of mutations, drug treatments and sensitivity analyses. It offers a framework for automated production of theoretical predictions.
Year
DOI
Venue
2017
10.1093/bioinformatics/btx123
BIOINFORMATICS
Field
DocType
Volume
Data mining,Visualization,Computer science,Boolean model,Software,Artificial intelligence,Computational biology,Machine learning
Journal
33
Issue
ISSN
Citations 
14
1367-4803
3
PageRank 
References 
Authors
0.41
6
9
Name
Order
Citations
PageRank
Gautier Stoll1454.44
Barthélémy Caron230.41
Eric Viara313422.41
Aurélien Dugourd430.75
Andrei Zinovyev528227.30
Aurélien Naldi638326.11
Guido Kroemer731.43
Emmanuel Barillot8950165.00
Laurence Calzone927617.90