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


Video introduction to GeneNetWeaver (GNW) and in silico benchmark generation and performance profiling of network inference methods.


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


Journal Articles

Fluorescence Behavioral Imaging (FBI) tracks identity in heterogeneous groups of Drosophila

P. P. Ramdya; T. Schaffter; D. Floreano; R. Benton 

PLOS One. 2012. Vol. 7, num. 11, p. e48381. DOI : 10.1371/journal.pone.0048381.

GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods

T. Schaffter; D. Marbach; D. Floreano 

Bioinformatics. 2011. Vol. 27, num. 16, p. 2263-2270. DOI : 10.1093/bioinformatics/btr373.

Revealing strengths and weaknesses of methods for gene network inference

D. Marbach; R. J. Prill; T. Schaffter; C. Mattiussi; D. Floreano et al. 

PNAS. 2010. Vol. 107, num. 14, p. 6286-6291. DOI : 10.1073/pnas.0913357107.

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

D. Marbach; T. Schaffter; C. Mattiussi; D. Floreano 

Journal of Computational Biology. 2009. Vol. 16, num. 2, p. 229-239. DOI : 10.1089/cmb.2008.09TT.


Numerical Integration of SDEs: A Short Tutorial

T. Schaffter 


Stochastic Simulations for DREAM4

T. Schaffter; D. Marbach