Ancherbak S., Kuruoglu E. E., Vingron M. Time-Dependent Gene Network Modelling by Sequential Monte Carlo. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13 (6) pp. 1183 - 1193. IEEE, 2016. |

Abstract (English) |
Most existing methods used for gene regulatory network modeling are dedicated to inference of steady state networks, which are prevalent over all time instants. However, gene interactions evolve over time. Information about the gene interactions in different stages of the life cycle of a cell or an organism is of high importance for biology. In the statistical graphical models literature, one can find a number of methods for studying steady-state network structures while the study of time varying networks is rather recent. A sequential Monte Carlo method, namely particle filtering (PF), provides a powerful tool for dynamic time series analysis. In this work, the PF technique is proposed for dynamic network inference and its potentials in time varying gene expression data tracking are demonstrated. The data used for validation are synthetic time series data available from the DREAM4 challenge, generated from known network topologies and obtained from transcriptional regulatory networks of S. cerevisiae. We model the gene interactions over the course of time with multivariate linear regressions where the parameters of the regressive process are changing over time. | |

URL: | http://ieeexplore.ieee.org/document/7312960/ | |

DOI: | 10.1109/TCBB.2015.2496301 | |

Subject | Gene interaction network Time varying network Multivariate analysis Dynamic Bayesian network Sequential Monte Carlo Particle filtering J.3 LIFE AND MEDICAL SCIENCES. Biology and genetics G.2.2 Graph Theory. Network problems G.3 PROBABILITY AND STATISTICS. Probabilistic algorithms (including Monte Carlo) G.3 PROBABILITY AND STATISTICS. Multivariate statistics G.3 PROBABILITY AND STATISTICS. Time series analysis 92D10 Genetics 62M10 Time series, auto-correlation, regression, etc. 92B15 General biostatistics |

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