ArticleBMC systems biology2009
How to identify essential genes from molecular networks?
Article in BMC systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 102 citations in OpenAlex.
- Exploring the multifactorial nature of autism through computational systems biology: calcium and the Rho GTPase RAC1 under the spotlight.Neuromolecular medicine · 2013Pooled it
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- Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions inFrontiers in microbiology · 2025Article
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- Network analyses in microbiome based on high-throughput multi-omics data.Briefings in bioinformatics · 2021Review
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- Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.PloS one · 2020Article
- Contrasting DNA methylation responses of inbred fish lines to different rearing environments.Epigenetics · 2019Article
- Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes.Bulletin of mathematical biology · 2019Article
- Systems Analysis of the 22q11.2 Microdeletion Syndrome Converges on a Mitochondrial Interactome Necessary for Synapse Function and Behavior.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs.Molecules (Basel, Switzerland) · 2019Article
- Consistency and differences between centrality measures across distinct classes of networks.PloS one · 2019Article
- Leveraging User-Friendly Network Approaches to Extract Knowledge From High-ThroughputFrontiers in genetics · 2019Review
- Identification of essential proteins based on a new combination of topological and biological features in weighted protein-protein interaction networks.IET systems biology · 2018Article
- Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents.PLoS computational biology · 2018Article
- A systematic survey of centrality measures for protein-protein interaction networks.BMC systems biology · 2018Article
- Accurate prediction of human essential genes using only nucleotide composition and association information.Bioinformatics (Oxford, England) · 2017Article
- Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.Frontiers in physiology · 2016Article
- A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes.PloS one · 2016Article
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion.
resultsBy simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for Saccharomyces cerevisiae, we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined.
conclusionThe method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes.
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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.