ArticleJournal of clinical bioinformatics2013
Protein co-expression network analysis (ProCoNA).
Article in Journal of clinical bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed.
- Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont.FEMS microbiology ecology · 2024Review
- The proteome and phosphoproteome of circulating extracellular vesicle-enriched preparations are associated with characteristic clinical features in type 1 diabetes.Frontiers in endocrinology · 2023Article
- Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches.Frontiers in physiology · 2022Review
- Article
- Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- High-Throughput Identification of the Plasma Proteomic Signature of Inflammatory Bowel Disease.Journal of Crohn's & colitis · 2019Article
- Functional homogeneity and specificity of topological modules in human proteome.BMC bioinformatics · 2019Article
- Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World.Proteomes · 2018Article
- MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain.EMBO molecular medicine · 2018Article
- From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data.EURASIP journal on bioinformatics & systems biology · 2017Review
- Huntington's disease blood and brain show a common gene expression pattern and share an immune signature with Alzheimer's disease.Scientific reports · 2017Article
- Undiscovered Physiology of Transcript and Protein Networks.Comprehensive Physiology · 2016Review
- Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens.PLoS computational biology · 2016Article
- Article
- A null model for Pearson coexpression networks.PloS one · 2015Article
- Multi-omic network signatures of disease.Frontiers in genetics · 2014Article
- Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803.Frontiers in bioengineering and biotechnology · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundBiological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology.
resultsWe have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions).
conclusionsBiological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.
Identifiers
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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.