Review on statistical methods for gene network reconstruction using expression data
Abstract
Network modeling has proven to be a fundamental tool in analyzing the inner workings of a cell. It has revolutionized our understanding of biological processes and made significant contributions to the discovery of disease biomarkers. Much effort has been devoted to reconstruct various types of biochemical networks using functional genomic datasets generated by high-throughput technologies. This paper discusses statistical methods used to reconstruct gene regulatory networks using gene expression data. In particular, we highlight progress made and challenges yet to be met in the problems involved in estimating gene interactions, inferring causality and modeling temporal changes of regulation behaviors. As rapid advances in technologies have made available diverse, large-scale genomic data, we also survey methods of incorporating all these additional data to achieve better, more accurate inference of gene networks.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Co-expressions are interpreted as correlations rather than causal regulations.
“Moreover, co-expressions are interpreted as correlations rather than causal regulations16.”
From Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data (2025) · cites this paper as [16]
Cites
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 · see citing works
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327 · see citing works
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903 · see citing works
Functional cartography of complex metabolic networks
Guimerà, R., & Nunes Amaral, L. A. (2005) · Nature · doi:10.1038/nature03288 · Cited by 3,983 · see citing works
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903 · see citing works
Zou, M., & Conzen, S. D. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bth463 · Cited by 587 · cited 1× · see citing works
“…rich statistical properties, parameter estimation is computationally expensive and often discretization of gene expression levels or simplifying graph topology based on prior knowledge is necessary to make parameter estimation feasible (Kim et al., 2004; Zou and Conzen, 2005; Zhu et al., 2010).”
BackgroundWolfe, C. J., Kohane, I. S., & Butte, A. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-227 · Cited by 422 · see citing works
A primer on learning in Bayesian networks for computational biology
Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030129 · Cited by 311 · see citing works
“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
Gillis, J., & Pavlidis, P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002444 · Cited by 214 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230 · see citing works
Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data
Yuan, Q., & Duren, Z. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02182-7 · Cited by 118 · see citing works
“Moreover, co-expressions are interpreted as correlations rather than causal regulations 16 .”
Signaling networks in inflammatory pathways and risk for suicidal behavior
Rengasamy, M., Zhong, Y., Marsland, A., Chen, K., Douaihy, A., Brent, D., & Melhem, N. M. (2020) · Brain, Behavior, & Immunity - Health · doi:10.1016/j.bbih.2020.100122 · Cited by 21 · see citing works
- Authors
- Wang, Y. X. R., & Huang, H.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Huang, Haiyan
- Venue
- Journal of Theoretical Biology
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 362
- Pages
- 53-61
- ISSN
- 0022-5193 (ISSN-L)
- OpenAlex
- W2015538881
- Cited by
- 182
- References
- 10
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 13 · 2023: 4 · 2022: 18 · 2021: 17 · 2020: 16 · 2019: 22 · 2018: 21 · 2017: 27 · 2016: 16 · 2015: 13 · 2014: 3
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Theoretical Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Human Genome Research Institute (U01HG007031)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- gene regulatory network inference, gene network reconstruction, gene expression data, causal inference, multi-omics integration, disease biomarkers
- MeSH terms
- Algorithms, Bayes Theorem, Gene Expression Regulation, Humans, Pattern Recognition, Automated, Software, Models, Statistical, Biomarkers, Cluster Analysis, Normal Distribution, Gene Expression Profiling, Genomics, 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
