Optimal Gene Regulatory Network Inference using the Boolean Kalman Filter and Multiple Model Adaptive Estimation Conference Paper

abstract

• 2015 IEEE. We propose a method for the inference of Boolean gene regulatory networks observed through noise. The algorithm is based on the optimal MMSE state estimator for a Boolean dynamical system, known as the Boolean Kalman filter (BKF). In the presence of partial knowledge about the network, a bank of BKFs representing the candidate models is run in parallel in a framework known as Multiple Model Adaptive Estimation (MMAE). Performance is investigated using a model of the p53-MDM2 negative feedback loop network, as well as application to large numbers of random networks in order to estimate average performance.

name of conference

• 2015 49th Asilomar Conference on Signals, Systems and Computers

authors

• Braga Neto, Ulisses

published proceedings

• 2015 49TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS

author list (cited authors)

• Imani, M., & Braga-Neto, U.

citation count

• 29

complete list of authors

• Imani, Mahdi||Braga-Neto, Ulisses

publication date

• January 2015

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference record / Asilomar Conference on Signals, Systems & Computers. Asilomar Conference on Signals, Systems & Computers  Journal

keywords

• Adaptive Filtering
• Boolean Kalman Filter
• Gene Regulatory Network
• Network Inference

Digital Object Identifier (DOI)

• 10.1109/acssc.2015.7421162

International Standard Book Number (ISBN) 13

• 9781467385763

start page

• 423

end page

• 427

volume

• 2016-February

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Facssc.2015.7421162