Refining modules to determine functionally significant clusters in molecular networks

Abstract

Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks. The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. Results were also compared with six existing methods available for clustering biological networks. In conclusion, the proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

COVID-19 Health Communication Networks on Twitter: Identifying Sources, Disseminators, and Brokers

“…Higher scores indicated that the clusters are more distinct or separated from one another (range 0=clusters completely overlap to 1=no connections between clusters). While modularity captures the extent to which clusters are distinct from one another, it is often unable to detect small clusters (Fortunato and Barthelemy, 2007;Kaalia and Rajapakse, 2019). To investigate the network in more depth, density between clusters was calculated as the sum of existing ties between two clusters divided by total possible number of ties between them (range 0=no connection to 1=complete connection).…”

Graph embeddings on gene ontology annotations for protein–protein interaction prediction

“…Based GO term-protein annotations, many research have employed information content (IC) of GO terms [22][23][24][25] to compute similarity between two proteins in order to predict PPI. These methods have succeeded in the development of protein-related tasks, including PPI prediction [26][27][28][29][30][31][32][33]. Despite their success, IC-based methods have been unable to fully capture functional properties of proteins and structural properties of PPIN.…”