Quantification of data extraction noise in probabilistic boolean network modeling

Ranadip Pal, Aniruddha Datta, Edward Dougherty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
DOIs
StatePublished - 2009
Event2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009 - Minneapolis, MN, United States
Duration: May 17 2009May 21 2009

Publication series

Name2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009

Conference

Conference2009 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2009
Country/TerritoryUnited States
CityMinneapolis, MN
Period05/17/0905/21/09

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