Cited by 308Gene Regulatory Network AnalysisGene expression and cancer classificationBayesian Modeling and Causal Inference

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Inferring gene networks from time series microarray data using dynamic Bayesian networks

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Kim, S. Y.·2003·Briefings in Bioinformatics

DOI 10.1093/bib/4.3.228

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Abstract

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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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
DOI: 10.1093/bib/4.3.228
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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Inferring gene networks from time series microarray data using dynamic Bayesian networks

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