ArticleBMC systems biology2012
A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data.
Article in BMC systems biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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84 citing papers in PubMed.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Inference of causal interaction networks of gut microbiota using transfer entropy.BMC genomics · 2025Article
- Constructing multilayer PPI networks based on homologous proteins and integrating multiple PageRank to identify essential proteins.BMC bioinformatics · 2025Article
- Identification of cancer driver genes based on hierarchical weak consensus model.Health information science and systems · 2024Article
- A protein network refinement method based on module discovery and biological information.BMC bioinformatics · 2024Article
- ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.BMC genomics · 2024Article
- Introducing critical proteins related to liver ischemia/reperfusion injury.Gastroenterology and hepatology from bed to bench · 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
- Morphological, physio-biochemical, and molecular indications of heat stress tolerance in cucumber.Scientific reports · 2023Article
- Identification of essential proteins based on edge features and the fusion of multiple-source biological information.BMC bioinformatics · 2023Article
- Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins.PloS one · 2023Article
- Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.Bioinformatics (Oxford, England) · 2022Article
- Article
- Identifying essential proteins from protein-protein interaction networks based on influence maximization.BMC bioinformatics · 2022Article
- A deep learning framework for identifying essential proteins based on multiple biological information.BMC bioinformatics · 2022Article
- Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy.Entropy (Basel, Switzerland) · 2022Article
- Article
- Review
- Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model.Frontiers in microbiology · 2022Article
24 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value.
resultsIn this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized α-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins.
conclusionsWe demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.
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