By Topic

Quantification of data extraction noise in probabilistic Boolean Network modeling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Pal, R. ; Electr. & Comput. Eng., Texas Tech Univ., Lubbock, TX, USA ; Datta, A. ; Dougherty, E.

Probabilistic Boolean Networks have served as the main model for studying the application of optimal intervention strategies to favorably affect system dynamics. The errors originating in the data extraction or network inference process prevent the accurate estimation of the state transition probabilities of the network. The mathematical characterization of the uncertainties will enable us to analyze the performance of intervention strategies derived without considering the uncertainties and assist in the design of control policies robust to those uncertainties. In this paper, we will quantify the errors due to data extraction noise and discretization and their effects on the state transition and steady state probabilities of the probabilistic Boolean network.

Published in:

Genomic Signal Processing and Statistics, 2009. GENSIPS 2009. IEEE International Workshop on

Date of Conference:

17-21 May 2009