Inferring gene networks from time series microarray data using dynamic Bayesian networks
Open accessAbstract
Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria for learning network structures are introduced from a Bayesian statistical viewpoint. This paper reviews the applications of DBNs over the past years. Real data applications for Saccharomyces cerevisiae time series gene expression data are also shown.
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Dynamic Bayesian networks can include cycles and may be constructed when time-course data are available.
“Unlike BNs, DBNs can include cycles and may be constructed when time-course data are available [89–92].”
From Reverse engineering and identification in systems biology: strategies, perspectives and challenges (2014) · cites this paper as [90]
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- Authors
- Kim, S. Y.
- Affiliations
- Institute of Medical Science, The University of Tokyo (JP)
- Corresponding author
- Kim, S. Y.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-01-01
- Volume
- 4
- Issue
- 3
- Pages
- 228-235
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2108579152
- Cited by
- 308
- References
- 17
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 2 · 2023: 13 · 2022: 2 · 2021: 10 · 2020: 10 · 2019: 9 · 2018: 10 · 2017: 7 · 2016: 9 · 2015: 16 · 2014: 20 · 2013: 16 · 2012: 22
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- dynamic Bayesian networks, gene regulatory network inference, time series microarray data, time delay, network structure learning, Bayesian network structure learning, Saccharomyces cerevisiae, gene expression time series
- MeSH terms
- Algorithms, Bayes Theorem, Computer Simulation, Mathematics, Models, Genetic, Saccharomyces cerevisiae, Time Factors, Gene Expression Regulation, Fungal, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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