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Towards a partitioning of the input space of Boolean networks: Variable selection using bagging

  • Queen's University Belfast
  • UMIT-The Health and Life Sciences University

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

Abstract

In this paper we present an algorithm that allows to select the input variables of Boolean networks from incomplete data. More precisely, sets of input variables, instead of single variables, are evaluated using mutual information to find the combination that maximizes the mutual information of input and output variables. To account for the incompleteness of the data bootstrap aggregation is used to find a stable solution that is numerically demonstrated to be superior in many cases to the solution found by using the complete data set all at once.

Original languageEnglish
Title of host publicationComplex Sciences - First International Conference, Complex 2009, Revised Papers
Pages715-723
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event1st International Conference on Complex Sciences: Theory and Applications, Complex 2009 - Shanghai, China
Duration: 23 Feb 200925 Feb 2009

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
NumberPART 1
Volume4 LNICST
ISSN (Print)1867-8211

Conference

Conference1st International Conference on Complex Sciences: Theory and Applications, Complex 2009
Country/TerritoryChina
CityShanghai
Period23/02/0925/02/09

Keywords

  • Boolean networks
  • Bootstrap aggregation
  • Causality
  • Mutual Information

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