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  Time-Dependent Gene Network Modelling by Sequential Monte Carlo

Ancherbak, S., Kuruoglu, E. E., & Vingron, M. (2016). Time-Dependent Gene Network Modelling by Sequential Monte Carlo. IEEE ACM Transactions on Computational Biology and Bioinformatics, 13(6), 1183-1193. doi:10.1109/TCBB.2015.2496301.

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Ancherbak.pdf (Verlagsversion), 2MB
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© 2016 IEEE
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http://www.ncbi.nlm.nih.gov/pubmed/26540693 (beliebiger Volltext)
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 Urheber:
Ancherbak, S.1, Autor
Kuruoglu, E. E.1, Autor
Vingron, M.2, Autor           
Affiliations:
1Istituto di Scienza e Tecnologie dell'Informazione, "A. Faedo", CNR Institute of the National Research Council of Italy, via G. Moruzzi 1, Pisa, Italy, ou_persistent22              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 Zusammenfassung: Most existing methods used for gene regulatory network modeling are dedicated to inference of steady state networks, which are prevalent over all time instants. However, gene interactions evolve over time. Information about the gene interactions in different stages of the life cycle of a cell or an organism is of high importance for biology. In the statistical graphical models literature, one can find a number of methods for studying steady-state network structures while the study of time varying networks is rather recent. A sequential Monte Carlo method, namely particle filtering (PF), provides a powerful tool for dynamic time series analysis. In this work, the PF technique is proposed for dynamic network inference and its potentials in time varying gene expression data tracking are demonstrated. The data used for validation are synthetic time series data available from the DREAM4 challenge, generated from known network topologies and obtained from transcriptional regulatory networks of S. cerevisiae. We model the gene interactions over the course of time with multivariate linear regressions where the parameters of the regressive process are changing over time.

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Sprache(n): eng - English
 Datum: 2016
 Publikationsstatus: Erschienen
 Seiten: 11
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 Art der Begutachtung: -
 Identifikatoren: PMID: 26540693
DOI: 10.1109/TCBB.2015.2496301
ISSN: 1557-9964 (Electronic)1545-5963 (Print)
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Titel: IEEE ACM Transactions on Computational Biology and Bioinformatics
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: New York, NY : IEEE Computer Society
Seiten: - Band / Heft: 13 (6) Artikelnummer: - Start- / Endseite: 1183 - 1193 Identifikator: ISSN: 1545-5963
CoNE: https://pure.mpg.de/cone/journals/resource/111098975410000