ArticleBMC bioinformatics2016
Predicting essential proteins based on subcellular localization, orthology and PPI networks.
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 24 papers.
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24 citing papers in PubMed, 88 citations in OpenAlex.
- 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
- Identification of essential proteins based on edge features and the fusion of multiple-source biological information.BMC bioinformatics · 2023Article
- DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.Bioinformatics (Oxford, England) · 2023Article
- Construction and analysis of expression profile of exosomal lncRNAs in pleural effusion in lung adenocarcinoma.Journal of clinical laboratory analysis · 2022Article
- Identifying essential proteins from protein-protein interaction networks based on influence maximization.BMC bioinformatics · 2022Article
- Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model.Frontiers in microbiology · 2022Article
- Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219.Molecular biotechnology · 2021Article
- A novel essential protein identification method based on PPI networks and gene expression data.BMC bioinformatics · 2021Article
- Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network.Frontiers in genetics · 2021Article
- A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.Frontiers in genetics · 2021Article
- An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity.Frontiers in aging neuroscience · 2021Article
- Comprehensive host-pathogen protein-protein interaction network analysis.BMC bioinformatics · 2020Article
- DeepHE: Accurately predicting human essential genes based on deep learning.PLoS computational biology · 2020Article
- DeepEP: a deep learning framework for identifying essential proteins.BMC bioinformatics · 2019Article
- Article
- A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein⁻Protein Interactive Networks.Genes · 2019Article
- A model to predict the function of hypothetical proteins through a nine-point classification scoring schema.BMC bioinformatics · 2019Article
- Quantifying Gene Essentiality Based on the Context of Cellular Components.Frontiers in genetics · 2019Article
- Feature Selection via Swarm Intelligence for Determining Protein Essentiality.Molecules (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEssential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods.
resultsComparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION.
conclusionsWe demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.