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

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

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 1, 2015 11:11 AM