ArticleBMC bioinformatics2016
Construction of dynamic probabilistic protein interaction networks for protein complex identification.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 34 citations in OpenAlex.
- DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.Genomics, proteomics & bioinformatics · 2025Article
- Sensitivity analysis on protein-protein interaction networks through deep graph networks.BMC bioinformatics · 2025Article
- Multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork embedding for protein complex identification.Briefings in bioinformatics · 2024Article
- Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.Health information science and systems · 2023Article
- A seed expansion-based method to identify essential proteins by integrating protein-protein interaction sub-networks and multiple biological characteristics.BMC bioinformatics · 2023Article
- A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks.Entropy (Basel, Switzerland) · 2023Article
- Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward.Computational and structural biotechnology journal · 2022Review
- Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks.BioMed research international · 2019Article
- Network Analyses in Plant Pathogens.Frontiers in microbiology · 2018Review
- An uncertain model-based approach for identifying dynamic protein complexes in uncertain protein-protein interaction networks.BMC genomics · 2017Article
- Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.PloS one · 2017Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.
resultsThe gene expression data under different time points and conditions can reveal the dynamic information of proteins. In this study, we used an active probability-based method to distinguish the active level of proteins at different active time points. We constructed dynamic probabilistic protein networks (DPPN) to integrate dynamic information of protein into static PPI networks. Based on DPPN, we subsequently proposed a novel method to identify protein complexes, which could effectively exploit topological structure as well as dynamic information of DPPN. We used three different yeast PPI datasets and gene expression data to construct three DPPNs. When applied to three DPPNs, many well-characterized protein complexes were accurately identified by this method.
conclusionThe shift from static PPI networks to dynamic PPI networks is essential to accurately identify protein complex. This method not only can be applied to identify protein complex, but also establish a framework to integrate dynamic information into static networks for other applications, such as pathway analysis.
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