Identifying communities from multiplex biological networks by randomized optimization of modularity.
F1000Res
; 7: 1042, 2018.
Article
in En
| MEDLINE
| ID: mdl-30210790
ABSTRACT
The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challenge benchmark, the results strongly depend on the selected GWAS dataset and enrichment p -value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at https//github.com/gilles-didier/MolTi-DREAM.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Software
/
Communicable Diseases
/
Quantitative Trait, Heritable
Type of study:
Clinical_trials
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
F1000Res
Year:
2018
Document type:
Article
Affiliation country:
France