The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders
Open accessAbstract
In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“At currently available sample sizes, networks are most reproducible at a module level35,38,39, then at the hub gene level41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes40,49.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [41]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Nor is there a guarantee that any of the hub genes is causal to a phenotype [78].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [78]
In the citing paper
“Coexpressed gene sets represent the outcome of transcriptional regulatory mechanisms that include transcription factors, chromatin conformation and latent factors that generate correlations, such as the proportion of different cell types present in the sampled tissue10.”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [10]
In the citing paper
“Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then finally at the level of precise protein connectivity rankings and module protein membership [61, 89–94].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [92]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,554
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
Research works printed but not linked: 2
- 10.1038/nature10166 · DOI · Paper page
- E.M. Izhikevich 2007 The MIT press Cambridge
Cited by
Sorted by citations
Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., Mohr, D. C., & Schatzberg, A. F. (2016) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2016.65 · Cited by 2,269
A scalable SCENIC workflow for single-cell gene regulatory network analysis
Van de Sande, B., Flerin, C., Davie, K., De Waegeneer, M., Hulselmans, G., Aibar, S., Seurinck, R., Saelens, W., Cannoodt, R., Rouchon, Q., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0336-2 · Cited by 1,494
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 995
Small-World Brain Networks Revisited
Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 887
Co-regulatory networks of human serum proteins link genetics to disease
Emilsson, V., Ilkov, M., Lamb, J. R., Finkel, N., Gudmundsson, E. F., Pitts, R., Hoover, H., Gudmundsdottir, V., Horman, S. R., Aspelund, T., et al. (2018) · Science · doi:10.1126/science.aaq1327 · Cited by 680
Metadata
- Authors
- Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E.
- Affiliations
- Allen Institute for Brain Science (US)
- University of Pittsburgh (US)
- Corresponding author
- Sibille, E.
- Venue
- Genes, Brain and Behavior
- Publisher
- Wiley
- Published
- 2013-12-10
- Volume
- 13
- Issue
- 1
- Pages
- 13-24
- ISSN
- 1601-183X (ISSN-L)
- 1601-1848
- PMC
- PMC3896950
- OpenAlex
- W1823837540
- Cited by
- 289
- References
- 105
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 14 · 2023: 14 · 2022: 25 · 2021: 38 · 2020: 21 · 2019: 24 · 2018: 29 · 2017: 32 · 2016: 34 · 2015: 20 · 2014: 9
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genes Brain & Behavior (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (MH084060, R01MH093723, R01MH077159, K02MH084060)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene co-expression network, neuropsychiatric disorders, hub genes, network modules, network meta-analysis, gene prioritization, network pharmacology, machine learning integration
- MeSH terms
- Animals, Humans, Nervous System Diseases, Transcription, Genetic, Gene Regulatory Networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
