A cubature Kalman filter approach for inferring gene regulatory networks using time series data

Amina Noor; Erchin Serpedin; Mohamed Nounou; Hazem Nounou

doi:10.1109/gensips.2011.6169432

A cubature Kalman filter approach for inferring gene regulatory networks using time series data

Amina Noor^*, Erchin Serpedin, Mohamed Nounou, Hazem Nounou

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

A novel technique for the inference of gene regulatory networks is proposed which utilizes cubature Kalman filter (CKF). The gene network is modeled using the state-space approach. A non-linear model for the evolution of gene expression is considered and the microarray data is assumed to follow a linear Gaussian model. CKF is used to estimate the hidden states as well as the unknown static parameters of the model. These parameters provide an insight into the regulatory relations among the genes. The proposed algorithm delievers superior performance than the linearization based extended Kalman filter (EKF) for synthetic as well as real world biological data.

Original language	English
Title of host publication	Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
Publisher	IEEE Computer Society
Pages	25-28
Number of pages	4
ISBN (Print)	9781467304900
DOIs	https://doi.org/10.1109/gensips.2011.6169432
Publication status	Published - 2011
Externally published	Yes
Event	2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 - San Antonio, TX, United States Duration: 4 Dec 2011 → 6 Dec 2011

Publication series

Name	Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
ISSN (Print)	2150-3001
ISSN (Electronic)	2150-301X

Conference

Conference	2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
Country/Territory	United States
City	San Antonio, TX
Period	4/12/11 → 6/12/11

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10.1109/gensips.2011.6169432

Cite this

Noor, A., Serpedin, E., Nounou, M., & Nounou, H. (2011). A cubature Kalman filter approach for inferring gene regulatory networks using time series data. In Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 (pp. 25-28). Article 6169432 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). IEEE Computer Society. https://doi.org/10.1109/gensips.2011.6169432

Noor, Amina ; Serpedin, Erchin ; Nounou, Mohamed et al. / A cubature Kalman filter approach for inferring gene regulatory networks using time series data. Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11. IEEE Computer Society, 2011. pp. 25-28 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics).

@inproceedings{c1ddf66189b74e60b470023f6ba98486,

title = "A cubature Kalman filter approach for inferring gene regulatory networks using time series data",

abstract = "A novel technique for the inference of gene regulatory networks is proposed which utilizes cubature Kalman filter (CKF). The gene network is modeled using the state-space approach. A non-linear model for the evolution of gene expression is considered and the microarray data is assumed to follow a linear Gaussian model. CKF is used to estimate the hidden states as well as the unknown static parameters of the model. These parameters provide an insight into the regulatory relations among the genes. The proposed algorithm delievers superior performance than the linearization based extended Kalman filter (EKF) for synthetic as well as real world biological data.",

author = "Amina Noor and Erchin Serpedin and Mohamed Nounou and Hazem Nounou",

year = "2011",

doi = "10.1109/gensips.2011.6169432",

language = "English",

isbn = "9781467304900",

series = "Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics",

publisher = "IEEE Computer Society",

pages = "25--28",

booktitle = "Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11",

address = "United States",

note = "2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 ; Conference date: 04-12-2011 Through 06-12-2011",

}

Noor, A, Serpedin, E, Nounou, M & Nounou, H 2011, A cubature Kalman filter approach for inferring gene regulatory networks using time series data. in Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11., 6169432, Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics, IEEE Computer Society, pp. 25-28, 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11, San Antonio, TX, United States, 4/12/11. https://doi.org/10.1109/gensips.2011.6169432

A cubature Kalman filter approach for inferring gene regulatory networks using time series data. / Noor, Amina; Serpedin, Erchin; Nounou, Mohamed et al.
Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11. IEEE Computer Society, 2011. p. 25-28 6169432 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A cubature Kalman filter approach for inferring gene regulatory networks using time series data

AU - Noor, Amina

AU - Serpedin, Erchin

AU - Nounou, Mohamed

AU - Nounou, Hazem

PY - 2011

Y1 - 2011

N2 - A novel technique for the inference of gene regulatory networks is proposed which utilizes cubature Kalman filter (CKF). The gene network is modeled using the state-space approach. A non-linear model for the evolution of gene expression is considered and the microarray data is assumed to follow a linear Gaussian model. CKF is used to estimate the hidden states as well as the unknown static parameters of the model. These parameters provide an insight into the regulatory relations among the genes. The proposed algorithm delievers superior performance than the linearization based extended Kalman filter (EKF) for synthetic as well as real world biological data.

AB - A novel technique for the inference of gene regulatory networks is proposed which utilizes cubature Kalman filter (CKF). The gene network is modeled using the state-space approach. A non-linear model for the evolution of gene expression is considered and the microarray data is assumed to follow a linear Gaussian model. CKF is used to estimate the hidden states as well as the unknown static parameters of the model. These parameters provide an insight into the regulatory relations among the genes. The proposed algorithm delievers superior performance than the linearization based extended Kalman filter (EKF) for synthetic as well as real world biological data.

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U2 - 10.1109/gensips.2011.6169432

DO - 10.1109/gensips.2011.6169432

M3 - Conference contribution

AN - SCOPUS:84863682910

SN - 9781467304900

T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics

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BT - Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11

PB - IEEE Computer Society

T2 - 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11

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Noor A, Serpedin E, Nounou M , Nounou H. A cubature Kalman filter approach for inferring gene regulatory networks using time series data. In Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11. IEEE Computer Society. 2011. p. 25-28. 6169432. (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). doi: 10.1109/gensips.2011.6169432

A cubature Kalman filter approach for inferring gene regulatory networks using time series data

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