Jmod: an extensible toolkit for modularity detection in complex networks

    

Biological interaction networks are often organized into groups or modules of related genes and proteins carrying out specific biological functions. Over the past few years, many methods have been proposed to rationally decompose those networks into functional modules, yet this challenging problem has not been fully satisfactorily solved.

Jmod is an extensible tool implementing state of the art modularity detection methods. Jmod implements three modularity detection methods, Newman’s spectral algorithm, a genetic algorithm-based method that we developed, and a brute force approach. We also developed a refinement technique called global Moving Vertex Method (gMVM) that can be used to further improve the performance of modularity detection methods, and provide an improved version of Newman’s spectral algorithm. The performance of the modularity detection and refinement methods in Jmod have been evaluated using biological and in silico benchmarks.

 
> Author’s project web page

In silico benchmark generation and performance profiling of network inference methods
Java library for simulating stochastic differential equations
Observation and interaction in experimental environments

Publications

Journal Articles

T. Schaffter; D. Marbach; D. Floreano : GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods; Bioinformatics. 2011. DOI : 10.1093/bioinformatics/btr373.

Reports

T. Schaffter : Numerical Integration of SDEs: A Short Tutorial. 2010.
T. Schaffter; D. Marbach : Stochastic Simulations for DREAM4. 2009.