A Cubature Kalman Filter Approach for Inferring Gene Regulatory Networks Using Time Series Data Conference Paper

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. 2011 IEEE.

name of conference

• 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

authors

• Nounou, Hazem
• Nounou, Mohamed
• Serpedin, Erchin

published proceedings

• 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

altmetric score

• 1

author list (cited authors)

• Noor, A., Serpedin, E., Nounout, M., & Nounou, H.

citation count

• 2

complete list of authors

• Noor, Amina||Serpedin, Erchin||Nounout, Mohamed||Nounou, Hazem

publication date

• December 2011

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics  Journal

keywords

• 31 Biological Sciences
• 3102 Bioinformatics And Computational Biology
• Biotechnology
• Genetics

Digital Object Identifier (DOI)

• 10.1109/gensips.2011.6169432

International Standard Book Number (ISBN) 13

• 9781467304900

start page

• 25

end page

• 28

URL

• http://dx.doi.org/10.1109/gensips.2011.6169432