CLASSIFICATION OF GAUSSIAN TRAJECTORIES WITH MISSING DATA IN BOOLEAN GENE REGULATORY NETWORKS Conference Paper

abstract

• 2017 IEEE. This paper studies the classification of gene regulatory networks (GRNs) modeled by probabilistic Boolean networks (PBNs). After observing Gaussian expression values of n genes at m consecutive time points, with consideration of missing data, an algorithm based on expectation maximization (EM) is proposed to estimate the parameters and infer the unknown parts of the networks in the maximum likelihood (ML) sense. Then the estimated values are plugged in to the Bayes classifier, which is optimal, and the performance of the classifier is investigated through various simulations.

name of conference

• 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

authors

• Braga Neto, Ulisses
• Dougherty, Edward

published proceedings

• 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)

author list (cited authors)

• Karbalayghareh, A., Braga-Neto, U., & Dougherty, E. R.

citation count

• 6

complete list of authors

• Karbalayghareh, Alireza||Braga-Neto, Ulisses||Dougherty, Edward R

publication date

• January 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference)  Journal

keywords

• Bayes Classifier
• Expectation Maximization
• Gene Regulatory Network
• Missing Trajectory Data
• Probabilistic Boolean Network
• Trajectory Classification

Digital Object Identifier (DOI)

• 10.1109/icassp.2017.7952322

International Standard Book Number (ISBN) 13

• 9781509041176

start page

• 1078

end page

• 1082

URL

• http://dx.doi.org/10.1109/icassp.2017.7952322