ArticlePloS one2016
A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- A protein network refinement method based on module discovery and biological information.BMC bioinformatics · 2024Article
- An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork.PloS one · 2024Article
- Identification of essential proteins based on edge features and the fusion of multiple-source biological information.BMC bioinformatics · 2023Article
- Potential Biomarkers and Drugs for Nanoparticle-Induced Cytotoxicity in the Retina: Based on Regulation of Inflammatory and Apoptotic Genes.International journal of environmental research and public health · 2022Article
- CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information.Interdisciplinary sciences, computational life sciences · 2021Review
- A Novel Collaborative Filtering Model-Based Method for Identifying Essential Proteins.Frontiers in genetics · 2021Review
- Comprehensive host-pathogen protein-protein interaction network analysis.BMC bioinformatics · 2020Article
- Computational Identification of Metabolic Pathways ofInternational journal of genomics · 2019Article
- Identification of essential proteins based on a new combination of topological and biological features in weighted protein-protein interaction networks.IET systems biology · 2018Article
- Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.Bioinformatics (Oxford, England) · 2018Article
- A new method for predicting essential proteins based on participation degree in protein complex and subgraph density.PloS one · 2018Article
- In Silico Knockout Screening ofBioMed research international · 2018Article
- Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.PloS one · 2018Article
- A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks.Scientific reports · 2017Article
- DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.PloS one · 2017Article
- A new computational strategy for identifying essential proteins based on network topological properties and biological information.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.