Inference of genetic regulatory networks using regularized likelihood with covariance estimation

We cast the problem of reverse-engineering the connectivity matrix of genetic regulatory networks from a limited number of measurements as a regularized multivariate regression problem. The regularization term incorporates the prior knowledge of sparsity of genetic regulatory networks. Moreover, the...

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Bibliographic Details
Published in2012 IEEE Statistical Signal Processing Workshop (SSP) pp. 560 - 563
Main Authors Rasool, G., Bouaynaya, N., Fathallah-Shaykh, H. M., Schonfeld, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2012
Subjects
convex optimization
Correlation
Covariance matrix
Estimation
Gene regulatory network
Genetics
maximum likelihood estimation
Measurement uncertainty
multivariate regression
Noise
Size measurement
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Summary:We cast the problem of reverse-engineering the connectivity matrix of genetic regulatory networks from a limited number of measurements as a regularized multivariate regression problem. The regularization term incorporates the prior knowledge of sparsity of genetic regulatory networks. Moreover, the genetic profiles within a measurement are assumed to be correlated with a full covariance structure. The proposed algorithm computes a sparse estimate of the connectivity matrix that accounts for correlated errors using regularized likelihood. We show that the joint estimation of the connectivity and covariance matrices improves the estimation of the network connectivity as compared to the assumption of uncorrelated measurements. Our algorithm has ln(ln(N)) sampling complexity. We test and validate our approach using synthetically generated networks.
ISBN:9781467301824
1467301825
ISSN:2373-0803
2693-3551
DOI:10.1109/SSP.2012.6319759