MONET: a toolbox integrating top-performing methods for network modularisation
Mattia TomasoniiD, Sergio GómeziD, Jake CrawfordiD, Weijia ZhangiD, Sarvenaz Choobdar, Daniel MarbachiD, Sven BergmanniD
Abstract
SummaryWe define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful bio-markers. To this end, we launched the \"Disease Module Identification (DMI) DREAM Challenge\", a community effort to build and evaluate unsupervised molecular network modularisation algorithms (Choobdar et al., 2018). Here we present MONET, a toolbox providing easy and unified access to the three top methods from the DMI DREAM Challenge for the bioinformatics community.\n\nAvailability and ImplementationMONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git\n\nContactmattia.tomasoni@unil.ch (MT); sven.bergmann@unil.ch (SB)
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 31
- FWCI
- 1.22
- Field percentile
- top 23%
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Assessment of network module identification across complex diseases
Sarvenaz Choobdar, Mehmet Eren Ahsen, … Daniel Marbach·Nature MethodsPeer-reviewed·Aug 30, 2019
- Inferring and Evaluating Network Medicine-Based Disease Modules with Nextflow
Johannes Kersting, Chloé Bûcheron, … Markus List·BioinformaticsPeer-reviewed·Jul 1, 2026
- MoDentify: a tool for phenotype-driven module identification in multilevel metabolomics networks
Kieu Trinh Do, Livia Dahlia Rasp, … Jan Krumsiek·BioinformaticsPeer-reviewed·Jul 19, 2018
- Identification of Disease Modules Using Higher-Order Network Structure
Pramesh Singh, Hannah E. Kuder, Anna Ritz·Bioinformatics AdvancesPeer-reviewed·Jan 1, 2023
- Refining modules to determine functionally significant clusters in molecular networks
Rama Kaalia, Jagath Chandana Rajapakse·BMC GenomicsPeer-reviewed·Dec 1, 2019
- ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.
Sebastian Vlaic, Theresia Conrad, … Stefan Schuster·Scientific ReportsPeer-reviewed·Jan 5, 2018