ArticlePloS one2018
Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- ELE: Estimating tissue-specific long noncoding RNA gene essentiality using graph neural networks.Quantitative biology (Beijing, China) · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A deep ensemble framework for human essential gene prediction by integrating multi-omics data.Scientific reports · 2025Article
- Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.Health information science and systems · 2023Article
- Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins.PloS one · 2023Article
- Article
- An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information.BMC bioinformatics · 2021Article
- CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information.Interdisciplinary sciences, computational life sciences · 2021Review
- Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning.International journal of molecular sciences · 2021Article
- Prediction of Human-Bioinformatics and biology insights · 2021Article
- Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- A Novel Collaborative Filtering Model-Based Method for Identifying Essential Proteins.Frontiers in genetics · 2021Review
- DeepHE: Accurately predicting human essential genes based on deep learning.PLoS computational biology · 2020Article
- Identifying essential proteins in dynamic protein networks based on an improved h-index algorithm.BMC medical informatics and decision making · 2020Article
- Heterogeneous Network Model to Identify Potential Associations BetweenInternational journal of molecular sciences · 2020Article
- A network analysis revealed the essential and common downstream proteins related to inguinal hernia.PloS one · 2020Article
- Dependency Between Protein-Protein Interactions and Protein Variability and Evolutionary Rates in Vertebrates: Observed Relationships and Stochastic Modeling.Journal of molecular evolution · 2019Article
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3 authors.
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Abstract
Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein's essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of Saccharomyces cerevisiae. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.
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