Signal Processing - Special issue: Genomic signal processing
Convex Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inference of gene regulatory networks based on a universal minimum description length
EURASIP Journal on Bioinformatics and Systems Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Reverse engineering of gene regulatory networks: a comparative study
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
IEEE Transactions on Signal Processing - Part II
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.