ArticleNucleic acids research2012
An iterative network partition algorithm for accurate identification of dense network modules.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Network Properties of Cancer Prognostic Gene Signatures in the Human Protein Interactome.Genes · 2020Article
- Genome-wide analysis of yeast expression data based on a priori generated co-regulation cliques.Microbial cell (Graz, Austria) · 2019Article
- Genome-Wide Identification of Regulatory Sequences Undergoing Accelerated Evolution in the Human Genome.Molecular biology and evolution · 2016Article
- LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.Scientific reports · 2016Article
- Predicting Diagnostic Gene Biomarkers for Non-Small-Cell Lung Cancer.BioMed research international · 2016Article
- Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach.BioMed research international · 2015Article
- Gene co-expression modules as clinically relevant hallmarks of breast cancer diversity.PloS one · 2014Article
- Optimization criteria and biological process enrichment in homologous multiprotein modules.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
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4 authors.
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Abstract
A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks.
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