Identifying periodicity of microarray gene expression profiles by autoregressive modeling and spectral estimation

Tsz-Yan Tang; Hong Yan

doi:10.1109/ICMLC.2010.5580747

Identifying periodicity of microarray gene expression profiles by autoregressive modeling and spectral estimation

Tsz-Yan Tang
, Hong Yan

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

We proposed an effective algorithm to analyze the periodicity of noisy microarray time series data. Each DNA microarray chip produces thousands of gene expressions. The data have a high level of noise, which make it a challenge to identity characteristics of the genes. Our algorithm is based on singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectral estimation. We have applied our algorithm to simulated noisy gene expression profiles and Plasmodium falciparum data, and the result shows that the algorithm is able to remove noise such that periodic genes expression profiles can be identified accurately. © 2010 IEEE.

Original language	English
Title of host publication	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages	3062-3066
Volume	6
DOIs	https://doi.org/10.1109/ICMLC.2010.5580747
Publication status	Published - 2010
Event	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China Duration: 11 Jul 2010 → 14 Jul 2010

Publication series

Name
Volume	6

Conference

Conference	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Place	China
City	Qingdao
Period	11/07/10 → 14/07/10

Research Keywords

Autoregressive (AR) model
Plasmodium falciparum
Singular spectrum analysis (SSA)
Singular value decomposition (SVD)
Time series data

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10.1109/ICMLC.2010.5580747

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@inproceedings{f5db3c4b85b5407dae550d149b5572ba,

title = "Identifying periodicity of microarray gene expression profiles by autoregressive modeling and spectral estimation",

abstract = "We proposed an effective algorithm to analyze the periodicity of noisy microarray time series data. Each DNA microarray chip produces thousands of gene expressions. The data have a high level of noise, which make it a challenge to identity characteristics of the genes. Our algorithm is based on singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectral estimation. We have applied our algorithm to simulated noisy gene expression profiles and Plasmodium falciparum data, and the result shows that the algorithm is able to remove noise such that periodic genes expression profiles can be identified accurately. {\textcopyright} 2010 IEEE.",

keywords = "Autoregressive (AR) model, Plasmodium falciparum, Singular spectrum analysis (SSA), Singular value decomposition (SVD), Time series data",

author = "Tsz-Yan Tang and Hong Yan",

year = "2010",

doi = "10.1109/ICMLC.2010.5580747",

language = "English",

isbn = "9781424465262",

volume = "6",

pages = "3062--3066",

booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

note = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 ; Conference date: 11-07-2010 Through 14-07-2010",

}

Tang, T-Y & Yan, H 2010, Identifying periodicity of microarray gene expression profiles by autoregressive modeling and spectral estimation. in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. vol. 6, 5580747, pp. 3062-3066, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, 11/07/10. https://doi.org/10.1109/ICMLC.2010.5580747

Identifying periodicity of microarray gene expression profiles by autoregressive modeling and spectral estimation. / Tang, Tsz-Yan; Yan, Hong.
2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 6 2010. p. 3062-3066 5580747.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Identifying periodicity of microarray gene expression profiles by autoregressive modeling and spectral estimation

AU - Tang, Tsz-Yan

AU - Yan, Hong

PY - 2010

Y1 - 2010

N2 - We proposed an effective algorithm to analyze the periodicity of noisy microarray time series data. Each DNA microarray chip produces thousands of gene expressions. The data have a high level of noise, which make it a challenge to identity characteristics of the genes. Our algorithm is based on singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectral estimation. We have applied our algorithm to simulated noisy gene expression profiles and Plasmodium falciparum data, and the result shows that the algorithm is able to remove noise such that periodic genes expression profiles can be identified accurately. © 2010 IEEE.

AB - We proposed an effective algorithm to analyze the periodicity of noisy microarray time series data. Each DNA microarray chip produces thousands of gene expressions. The data have a high level of noise, which make it a challenge to identity characteristics of the genes. Our algorithm is based on singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectral estimation. We have applied our algorithm to simulated noisy gene expression profiles and Plasmodium falciparum data, and the result shows that the algorithm is able to remove noise such that periodic genes expression profiles can be identified accurately. © 2010 IEEE.

KW - Autoregressive (AR) model

KW - Plasmodium falciparum

KW - Singular spectrum analysis (SSA)

KW - Singular value decomposition (SVD)

KW - Time series data

UR - https://www.scopus.com/pages/publications/78149339378

UR - https://www.scopus.com/pages/publications/78149339378#tab=impact

U2 - 10.1109/ICMLC.2010.5580747

DO - 10.1109/ICMLC.2010.5580747

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424465262

VL - 6

SP - 3062

EP - 3066

BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

T2 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

Y2 - 11 July 2010 through 14 July 2010

ER -

Identifying periodicity of microarray gene expression profiles by autoregressive modeling and spectral estimation

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Research Keywords

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