@inproceedings{3f8abc143dad4a839984dc63992cd7a3,
title = "Switching Langevin Dynamics for Gene Regulatory Networks",
abstract = "We consider the problem of estimating a gene regulatory network under switching noise dynamics. Several stochastic models based on discretized Langevin dynamics are introduced to model different types of intrinsic and extrinsic noise in gene regulatory networks. We propose a Bayesian technique that chooses the appropriate dynamical system to describe the noise. The resulting Bayesian hierarchical model uses a categorical distribution to probabilistically select the dynamical system. We then exploit particle filtering to infer the noise type as well as the state trajectory. We demonstrate the advantage of our proposed method through simulations.",
keywords = "Bayesian inference, gene regulatory networks, par-ticle filter, stochastic differential equations, stochastic modeling",
author = "Nayely Velez-Cruz and Bahman Moraffah and Antonia Papandreou-Suppappola",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 ; Conference date: 31-10-2022 Through 02-11-2022",
year = "2022",
doi = "10.1109/IEEECONF56349.2022.10051930",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1316--1320",
editor = "Matthews, {Michael B.}",
booktitle = "56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022",
}