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Bayesian estimation of the discrete coefficient of determination

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, January 2016
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Title
Bayesian estimation of the discrete coefficient of determination
Published in
EURASIP Journal on Bioinformatics & Systems Biology, January 2016
DOI 10.1186/s13637-015-0035-4
Pubmed ID
Authors

Ting Chen, Ulisses M. Braga-Neto

Abstract

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 67%
Student > Ph. D. Student 1 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 33%
Computer Science 1 33%
Medicine and Dentistry 1 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 January 2016.
All research outputs
#11,076,454
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#39
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Outputs of similar age
#272,972
of 331,879 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#2
of 2 outputs
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