Towards a Partitioning of the Input Space of Boolean Networks: Variable Selection Using Bagging

Frank Emmert-Streib; Matthias Dehmer

Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1

Research Article

Towards a Partitioning of the Input Space of Boolean Networks: Variable Selection Using Bagging

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@INPROCEEDINGS{10.1007/978-3-642-02466-5_69,
    author={Frank Emmert-Streib and Matthias Dehmer},
    title={Towards a Partitioning of the Input Space of Boolean Networks: Variable Selection Using Bagging},
    proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1},
    proceedings_a={COMPLEX PART 1},
    year={2012},
    month={5},
    keywords={Bootstrap aggregation Mutual Information Boolean networks Causality},
    doi={10.1007/978-3-642-02466-5_69}
}

Frank Emmert-Streib
Matthias Dehmer
Year: 2012
Towards a Partitioning of the Input Space of Boolean Networks: Variable Selection Using Bagging
COMPLEX PART 1
Springer
DOI: 10.1007/978-3-642-02466-5_69

Frank Emmert-Streib¹^,*, Matthias Dehmer²^,*

1: Queen’s University Belfast
2: Institute for Bioinformatics and Translational Research, UMIT

*Contact email: v@bio-complexity.com, Matthias.Dehmer@umit.at

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.

Keywords: Bootstrap aggregation Mutual Information Boolean networks Causality

Published: 2012-05-11

: http://dx.doi.org/10.1007/978-3-642-02466-5_69