Standard

Noise model estimation with application to gene expression. / Zhornikova, P.; Golyandina, N.; Spirov, A.

In: Journal of Bioinformatics and Computational Biology, Vol. 17, No. 2, 1950009, 01.04.2019.

Research output: Contribution to journal › Article › peer-review

Harvard

Zhornikova, P, Golyandina, N & Spirov, A 2019, 'Noise model estimation with application to gene expression', Journal of Bioinformatics and Computational Biology, vol. 17, no. 2, 1950009. https://doi.org/10.1142/S0219720019500094

APA

Zhornikova, P., Golyandina, N., & Spirov, A. (2019). Noise model estimation with application to gene expression. Journal of Bioinformatics and Computational Biology, 17(2), [1950009]. https://doi.org/10.1142/S0219720019500094

Vancouver

Zhornikova P, Golyandina N, Spirov A. Noise model estimation with application to gene expression. Journal of Bioinformatics and Computational Biology. 2019 Apr 1;17(2). 1950009. https://doi.org/10.1142/S0219720019500094

Author

Zhornikova, P. ; Golyandina, N. ; Spirov, A. / Noise model estimation with application to gene expression. In: Journal of Bioinformatics and Computational Biology. 2019 ; Vol. 17, No. 2.

BibTeX

@article{59ec75005c80402d9d80a163f316b30e,
title = "Noise model estimation with application to gene expression",
abstract = "Algorithms for the estimation of noise level and the detection of noise model are proposed. They are applied to gene expression data for Drosophila embryos. The 2D data on gene expression and the extracted 1D profiles are considered. Since the 1D data contain processing errors, an algorithm for separation of these processing errors is constructed to estimate the biological noise level. An approach to discrimination between the additive and multiplicative models is suggested for the 1D and 2D cases. Singular spectrum analysis and its 2D extension are exploited for the pattern extraction. The algorithms are tested on artificial data similar to the real data. Comparison of the results, which are obtained by the 1D and 2D methods, is performed for Kr{\"u}ppel and giant genes.",
keywords = "Nucleus-to-nucleus variability, gene expression, noise model, singular spectrum analysis, REGRESSION",
author = "P. Zhornikova and N. Golyandina and A. Spirov",
year = "2019",
month = apr,
day = "1",
doi = "10.1142/S0219720019500094",
language = "English",
volume = "17",
journal = "Journal of Bioinformatics and Computational Biology",
issn = "0219-7200",
publisher = "WORLD SCIENTIFIC PUBL CO PTE LTD",
number = "2",

}

RIS

TY - JOUR

T1 - Noise model estimation with application to gene expression

AU - Zhornikova, P.

AU - Golyandina, N.

AU - Spirov, A.

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Algorithms for the estimation of noise level and the detection of noise model are proposed. They are applied to gene expression data for Drosophila embryos. The 2D data on gene expression and the extracted 1D profiles are considered. Since the 1D data contain processing errors, an algorithm for separation of these processing errors is constructed to estimate the biological noise level. An approach to discrimination between the additive and multiplicative models is suggested for the 1D and 2D cases. Singular spectrum analysis and its 2D extension are exploited for the pattern extraction. The algorithms are tested on artificial data similar to the real data. Comparison of the results, which are obtained by the 1D and 2D methods, is performed for Krüppel and giant genes.

AB - Algorithms for the estimation of noise level and the detection of noise model are proposed. They are applied to gene expression data for Drosophila embryos. The 2D data on gene expression and the extracted 1D profiles are considered. Since the 1D data contain processing errors, an algorithm for separation of these processing errors is constructed to estimate the biological noise level. An approach to discrimination between the additive and multiplicative models is suggested for the 1D and 2D cases. Singular spectrum analysis and its 2D extension are exploited for the pattern extraction. The algorithms are tested on artificial data similar to the real data. Comparison of the results, which are obtained by the 1D and 2D methods, is performed for Krüppel and giant genes.

KW - Nucleus-to-nucleus variability

KW - gene expression

KW - noise model

KW - singular spectrum analysis

KW - REGRESSION

UR - http://www.scopus.com/inward/record.url?scp=85065174324&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/noise-model-estimation-application-gene-expression

U2 - 10.1142/S0219720019500094

DO - 10.1142/S0219720019500094

M3 - Article

VL - 17

JO - Journal of Bioinformatics and Computational Biology

JF - Journal of Bioinformatics and Computational Biology

SN - 0219-7200

IS - 2

M1 - 1950009

ER -

ID: 36504649