Computationally Efficient Experimental Design Strategy for Reducing Gene Network Uncertainty Conference Paper

abstract

• 2014 IEEE. In this work, we present a computationally efficient method for selecting experiments that can effectively reduce the uncertainty in gene regulatory networks (GRNs). The proposed method prioritizes potential experiments based on the mean objective cost of uncertainty (MOCU) that is expected to remain after performing the experiments. A network reduction scheme is used to approximately estimate the MOCU at a reduced computational cost without disrupting the ranking of potential experiments. The effectiveness of our method is demonstrated through simulations.

name of conference

• 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

authors

• Dougherty, Edward
• Yoon, Byung-Jun

published proceedings

• 2014 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP)

author list (cited authors)

• Dehghannasiri, R., Yoon, B., & Dougherty, E. R.

citation count

• 0

complete list of authors

• Dehghannasiri, Roozbeh||Yoon, Byung-Jun||Dougherty, Edward R

publication date

• January 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Gene Regulatory Networks (grns)
• Mean Objective Cost Of Uncertainty (mocu)
• Objective-based Network Reduction
• Optimal Experimental Design

Digital Object Identifier (DOI)

• 10.1109/globalsip.2014.7032352

International Standard Book Number (ISBN) 13

• 9781479970889

start page

• 1380

end page

• 1381

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fglobalsip.2014.7032352