» Articles » PMID: 29170401

Functional Diversity of Topological Modules in Human Protein-protein Interaction Networks

Overview
Journal Sci Rep
Specialty Science
Date 2017 Nov 25
PMID 29170401
Citations 12
Authors
Affiliations
Soon will be listed here.
Abstract

A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity.

Citing Articles

KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.

Wang X, Yang K, Jia T, Gu F, Wang C, Xu K Brief Bioinform. 2024; 25(3).

PMID: 38605639 PMC: 11009469. DOI: 10.1093/bib/bbae161.


Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease.

Rohl A, Baek S, Kachroo P, Morrow J, Tantisira K, Silverman E Respir Res. 2022; 23(1):69.

PMID: 35331221 PMC: 8944072. DOI: 10.1186/s12931-022-01963-5.


Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins.

Jeong H, Kim Y, Jung Y, Kang D, Cho Y Entropy (Basel). 2021; 23(10).

PMID: 34681995 PMC: 8534328. DOI: 10.3390/e23101271.


Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.

Arici M, Tuncbag N Front Mol Biosci. 2021; 8:666705.

PMID: 34676243 PMC: 8523993. DOI: 10.3389/fmolb.2021.666705.


Knowledge-Guided "Community Network" Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer.

Wang F, Han S, Yang J, Yan W, Hu G Cells. 2021; 10(2).

PMID: 33669233 PMC: 7919838. DOI: 10.3390/cells10020402.


References
1.
Ideker T, Sharan R . Protein networks in disease. Genome Res. 2008; 18(4):644-52. PMC: 3863981. DOI: 10.1101/gr.071852.107. View

2.
Xu J, Li Y . Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics. 2006; 22(22):2800-5. DOI: 10.1093/bioinformatics/btl467. View

3.
Schaefer C, Anthony K, Krupa S, Buchoff J, Day M, Hannay T . PID: the Pathway Interaction Database. Nucleic Acids Res. 2008; 37(Database issue):D674-9. PMC: 2686461. DOI: 10.1093/nar/gkn653. View

4.
Goh K, Cusick M, Valle D, Childs B, Vidal M, Barabasi A . The human disease network. Proc Natl Acad Sci U S A. 2007; 104(21):8685-90. PMC: 1885563. DOI: 10.1073/pnas.0701361104. View

5.
Sah P, Singh L, Clauset A, Bansal S . Exploring community structure in biological networks with random graphs. BMC Bioinformatics. 2014; 15:220. PMC: 4094994. DOI: 10.1186/1471-2105-15-220. View