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Inferring Boolean functions via higher-order correlations

Content Provider	Springer Nature Link
Author	Maucher, Markus Kracht, David V. Schober, Steffen Bossert, Martin Kestler, Hans A.
Copyright Year	2012
Abstract	Both the Walsh transform and a modified Pearson correlation coefficient can be used to infer the structure of a Boolean network from time series data. Unlike the correlation coefficient, the Walsh transform is also able to represent higher-order correlations. These correlations of several combined input variables with one output variable give additional information about the dependency between variables, but are also more sensitive to noise. Furthermore computational complexity increases exponentially with the order. We first show that the Walsh transform of order 1 and the modified Pearson correlation coefficient are equivalent for the reconstruction of Boolean functions. Secondly, we also investigate under which conditions (noise, number of samples, function classes) higher-order correlations can contribute to an improvement of the reconstruction process. We present the merits, as well as the limitations, of higher-order correlations for the inference of Boolean networks.
Starting Page	97
Ending Page	115
Page Count	19
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	29
Issue Number	1-2
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2012-12-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Boolean networks Systems biology Network inference Correlation Noise Walsh transform Fourier transform Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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