Designing Experiments for Optimal Reduction of Uncertainty in Gene Regulatory Networks Conference Paper

abstract

• One of the main issues in systems biology is limited resources for conducting biological experiments. Therefore, a strategy for prioritizing the experiments seems to be inevitable. Experimental design is the process of planning experiments in such a way to make experiments as informative as possible. In this work, we propose a novel strategy for designing effective experiments that can optimally reduce the uncertainty in gene regulatory networks, based on the concept of mean objective cost of uncertainty (MOCU). 2013 IEEE.

name of conference

• 2013 IEEE International Workshop on Genomic Signal Processing and Statistics

authors

• Dougherty, Edward
• Yoon, Byung-Jun

published proceedings

• 2013 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2013)

author list (cited authors)

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

citation count

• 1

complete list of authors

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

publication date

• January 2013

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics  Journal

keywords

• Boolean Networks
• Experimental Design
• Gene Regulatory Networks
• Mean Objective Cost Of Uncertainty (mocu)

Digital Object Identifier (DOI)

• 10.1109/gensips.2013.6735942

International Standard Book Number (ISBN) 13

• 9781479934621

start page

• 88

end page

• 89

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fgensips.2013.6735942