The DOI matches its Crossref record; 130 of the 132 references it cites were found in a registry or an index.
Gene regulatory network inference: Data integration in dynamic models—A review
Abstract
Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.
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“Overall, integration of multiple data types increases the accuracy of the resulting predictions [13, 147].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [147]
In the citing paper
“However, a focus on TFs is rarely sufficient, and integration of multiple data types is often required to increase the accuracy and usefulness of the resulting networks [13, 147].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [147]
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2 of the 132 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Westra, R, 2008. International Workshop on Gene Regulatory Network Inference, Jena, Personal Communication.
- Yong-A-Poi, J., 2008. Adaptive least Absolute Regression Network Analysis Improves Genetic Network Reconstruction by Employing Prior Knowledge (Master Thesis). Delft University of Technology, The Netherlands.
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Microbial interactions: from networks to models
Faust, K., & Raes, J. (2012) · Nature Reviews Microbiology · doi:10.1038/nrmicro2832 · Cited by 4,268
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
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Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00038 · Cited by 282
Metadata
- Authors
- Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R.
- Affiliations
- Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI) (DE)
- BioControl Jena (Germany) (DE)
- Corresponding author
- Guthke, Reinhard
- ORCID
- Hecker, Michael
- Venue
- Biosystems
- Publisher
- Elsevier BV
- Published
- 2009-04
- Volume
- 96
- Issue
- 1
- Pages
- 86-103
- ISSN
- 0303-2647 (ISSN-L)
- OpenAlex
- W1963522244
- Cited by
- 755
- References
- 132
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 37 · 2023: 22 · 2022: 30 · 2021: 33 · 2020: 32 · 2019: 48 · 2018: 63 · 2017: 50 · 2016: 62 · 2015: 71 · 2014: 56 · 2013: 62 · 2012: 61
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biosystems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (0313078D, 0313692D)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- gene regulatory network inference, data integration, gene expression microarrays, genome sequence data, genome-wide data, learning algorithms
- MeSH terms
- Computer Simulation, Database Management Systems, Gene Expression Regulation, Models, Biological, Signal Transduction, Systems Integration, Proteome, Gene Expression Profiling, Databases, Protein, Systems Biology
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