This repository holds the source code for DreamDMI, a Linux/macOS command-line tool for Disease Module Identification in molecular networks, leveraging the top performing methods of the Disease Module Identification (DMI) DREAM Challenge (https://www.synapse.org/modulechallenge)
- K1: Kernel clustering optimisation algorithm, https://www.synapse.org/#!Synapse:syn7349492/wiki/407359
- M1: Modularity optimization algorithm, https://www.synapse.org/#!Synapse:syn7352969/wiki/407384
- R1: Random-walk-based algorithm, https://www.synapse.org/#!Synapse:syn7286597/wiki/406659
The source code is hosted at: https://github.com/mattiat/DREAM_DMI_Tool
Either docker or singularity must be installed. Please visit https://www.docker.com or http://singularity.lbl.gov
Some of the Methods may require large amount of resources, depending on your input.
The tool was tested on Ubuntu Linux 18.04, CentOS Linux 7.5 and macOS Sierra Version 10.12.
To install: ./install
To uninstall: ./uninstall
To run, invoke, from any location: dream_dmi --help
The format for the input network is the following: a tab-separated file containing one line for each edge. If an edge is connecting two nodes, nodeA and nodeB, with weight weight_AB, the file will contain the entry:
[nodeA] [nodeB] [weight_AB]
nodeA and nodeB are of type integer, weight_AB is of type float.
For an example, see the contents of test/system_test/input/.
see test/benchmarking
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases
Sarvenaz Choobdar, Mehmet E. Ahsen, Jake Crawford, Mattia Tomasoni, David Lamparter, Junyuan Lin, Benjamin Hescott, Xiaozhe Hu, Johnathan Mercer, Ted Natoli, Rajiv Narayan, The DREAM Module Identification Challenge Consortium, Aravind Subramanian, Gustavo Stolovitzky, Zoltán Kutalik, Kasper Lage, Donna K. Slonim, Julio Saez-Rodriguez, Lenore J. Cowen, Sven Bergmann, Daniel Marbach. bioRxiv 265553 (2018). doi: https://doi.org/10.1101/265553