@inproceedings{93c66f0d574a4bce91314545d8f5bf4a,
title = "A Particle Gibbs Sampling Approach to Topology Inference in Gene Regulatory Networks",
abstract = "In this paper, we propose a novel Bayesian approach for estimating a gene network's topology using particle Gibbs sampling. The conditional posterior distributions of the unknowns in a state-space model describing the time evolution of gene expressions are derived and employed for exact Bayesian posterior inference. Specifically, the proposed scheme provides the joint posterior distribution of the unknown gene expressions, the adjacency matrix describing the topology of the network, and the coefficient matrix describing the strength of the gene interactions. We validate the proposed method with numerical simulations on synthetic data experiments.",
keywords = "Gibbs sampling, gene networks, network topology, particle filtering, state-space models",
author = "Marija Iloska and Yousef El-Laham and Bugallo, \{Monica F.\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",
year = "2020",
month = may,
doi = "10.1109/ICASSP40776.2020.9053525",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5855--5859",
booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",
}