PHENOTYPICALLY CONSTRAINED BOOLEAN NETWORK INFERENCE WITH PRESCRIBED STEADY STATES Conference Paper

abstract

• In this paper, we investigate a phenotypically constrained inference algorithm to reconstruct genetic regulatory networks modeled as Boolean networks (BNs). Based on a previous universal Minimum Description Length (uMDL) network inference algorithm, we study whether adding the prior information based on prescribed attractors or steady states can help better reconstruct the underlying gene regulatory relationships. Comparing the network inference performance with and without prescribed steady states, the experiments based on randomly generated networks as well as a metastatic melanoma network have shown that the phenotypically constrained inference obtains improved performance when we have small numbers of state transition observations. 2013 IEEE.

name of conference

• 2013 IEEE International Workshop on Genomic Signal Processing and Statistics

authors

• Dougherty, Edward
• Qian, Xiaoning

published proceedings

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

author list (cited authors)

• Qian, X., & Dougherty, E. R.

citation count

• 1

complete list of authors

• Qian, Xiaoning||Dougherty, Edward R

publication date

• November 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 Network
• Genetic Regulatory Network
• Network Inference
• Probabilistic Boolean Network

Digital Object Identifier (DOI)

• 10.1109/gensips.2013.6735938

International Standard Book Number (ISBN) 13

• 9781479934621

start page

• 82

end page

• 83

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fgensips.2013.6735938