ArticleBMC systems biology2010
Determining modular organization of protein interaction networks by maximizing modularity density.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- GNN4DM: a graph neural network-based method to identify overlapping functional disease modules.Bioinformatics (Oxford, England) · 2024Article
- Improving drug response prediction via integrating gene relationships with deep learning.Briefings in bioinformatics · 2024Article
- Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge.Frontiers in bioinformatics · 2021Article
- miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence.PloS one · 2021Article
- Significance-based multi-scale method for network community detection and its application in disease-gene prediction.PloS one · 2020Article
- Article
- Critical analysis of (Quasi-)Surprise for community detection in complex networks.Scientific reports · 2018Article
- Global vs local modularity for network community detection.PloS one · 2018Article
- Anti-triangle centrality-based community detection in complex networks.IET systems biology · 2014Article
- Applied graph-mining algorithms to study biomolecular interaction networks.BioMed research international · 2014Article
- Visualization of protein interaction networks: problems and solutions.BMC bioinformatics · 2013Article
- Discovering protein complexes in protein interaction networks via exploring the weak ties effect.BMC systems biology · 2012Article
- The effect of coupled stochastic processes in a two-state biochemical switch.Journal of biological physics · 2011Article
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4 authors.
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Abstract
backgroundWith ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable.
resultsThe purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network.
conclusionsOur analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method.
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