Estimation of gene expression by a bank of particle filters. Bugallo, M. F., Taşdemir, Ç., & Djurić, P. M. In *2015 23rd European Signal Processing Conference (EUSIPCO)*, pages 494-498, Aug, 2015.

Paper doi abstract bibtex

Paper doi abstract bibtex

This paper addresses the problem of joint estimation of time series of gene expressions and identification of the coefficients of gene interactions defining the network. The proposed method exploits a state-space structure describing the system so that a bank of particle filters can be used to efficiently track each of the time series separately. Since each gene interacts with some of the other genes, the individual filters need to exchange information about the states (genes) that they track. The analytical derivation of the posterior distribution of the states given the observed data allows for marginalization of the matrix describing the interactions in the network and for efficient implementation of the method. Computer simulations reveal a promising performance of the proposed approach when compared to the conventional particle filter that attempts to track the time series of all the genes and which, as a result, suffers from the curse-of-dimensionality.

@InProceedings{7362432, author = {M. F. Bugallo and Ç. Taşdemir and P. M. Djurić}, booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)}, title = {Estimation of gene expression by a bank of particle filters}, year = {2015}, pages = {494-498}, abstract = {This paper addresses the problem of joint estimation of time series of gene expressions and identification of the coefficients of gene interactions defining the network. The proposed method exploits a state-space structure describing the system so that a bank of particle filters can be used to efficiently track each of the time series separately. Since each gene interacts with some of the other genes, the individual filters need to exchange information about the states (genes) that they track. The analytical derivation of the posterior distribution of the states given the observed data allows for marginalization of the matrix describing the interactions in the network and for efficient implementation of the method. Computer simulations reveal a promising performance of the proposed approach when compared to the conventional particle filter that attempts to track the time series of all the genes and which, as a result, suffers from the curse-of-dimensionality.}, keywords = {biology;channel bank filters;estimation theory;matrix algebra;particle filtering (numerical methods);time series;particle filters;gene expression time series estimation;gene interactions;state-space structure;matrix marginalization;curse-of-dimensionality;Estimation;Gene expression;Covariance matrices;Time series analysis;Kalman filters;Approximation methods;Standards;Gene regulatory network;particle filtering;dimensionality reduction}, doi = {10.1109/EUSIPCO.2015.7362432}, issn = {2076-1465}, month = {Aug}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570105209.pdf}, }

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