ArticleBMC bioinformatics2021
A novel essential protein identification method based on PPI networks and gene expression data.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- CNN4Essential: a convolutional neural network model for predicting bacterial gene essentiality based on multi-feature fusion.BMC genomics · 2026Article
- In-silico identification of genetic variants associated with chronic lymphocytic leukemia for diagnostic and therapeutic applications.Scientific reports · 2026Article
- In-silico discovery of type-2 diabetes-causing host key genes that are associated with the complexity of monkeypox and repurposing common drugs.Briefings in bioinformatics · 2025Article
- PRODE recovers essential and context-essential genes through neighborhood-informed scores.Genome biology · 2025Article
- AttentionEP: Predicting essential proteins via fusion of multiscale features by attention mechanisms.Computational and structural biotechnology journal · 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
- 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
- Identification of essential proteins based on edge features and the fusion of multiple-source biological information.BMC bioinformatics · 2023Article
- Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits.Scientific reports · 2023Article
- Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.Vaccines · 2023Article
- A deep learning framework for identifying essential proteins based on multiple biological information.BMC bioinformatics · 2022Article
- Computational modeling of human-nCoV protein-protein interaction network.Methods (San Diego, Calif.) · 2022Article
- Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model.Frontiers in microbiology · 2022Article
- Review
- Combination of Enrichment Using Gene Ontology and Transcriptomic Analysis Revealed Contribution of Interferon Signaling to Severity of COVID-19.Interdisciplinary perspectives on infectious diseases · 2022Article
- A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.Frontiers in genetics · 2021Article
- Article
- Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network.Frontiers in genetics · 2021Article
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8 authors.
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Abstract
backgroundSome proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins.
resultsIn this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression.
conclusionsWe demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.
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