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The application of sparse estimation of covariance matrix to quadratic discriminant analysis

Overview of attention for article published in BMC Bioinformatics, February 2015
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Title
The application of sparse estimation of covariance matrix to quadratic discriminant analysis
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-014-0443-6
Pubmed ID
Authors

Jiehuan Sun, Hongyu Zhao

Abstract

Although Linear Discriminant Analysis (LDA) is commonly used for classification, it may not be directly applied in genomics studies due to the large p, small n problem in these studies. Different versions of sparse LDA have been proposed to address this significant challenge. One implicit assumption of various LDA-based methods is that the covariance matrices are the same across different classes. However, rewiring of genetic networks (therefore different covariance matrices) across different diseases has been observed in many genomics studies, which suggests that LDA and its variations may be suboptimal for disease classifications. However, it is not clear whether considering differing genetic networks across diseases can improve classification in genomics studies. We propose a sparse version of Quadratic Discriminant Analysis (SQDA) to explicitly consider the differences of the genetic networks across diseases. Both simulation and real data analysis are performed to compare the performance of SQDA with six commonly used classification methods. SQDA provides more accurate classification results than other methods for both simulated and real data. Our method should prove useful for classification in genomics studies and other research settings, where covariances differ among classes.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 5%
Unknown 19 95%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 4 20%
Student > Bachelor 4 20%
Student > Ph. D. Student 4 20%
Researcher 3 15%
Other 2 10%
Other 3 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 30%
Computer Science 4 20%
Mathematics 3 15%
Engineering 2 10%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 4 20%

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 18 February 2015.
All research outputs
#4,001,087
of 4,777,873 outputs
Outputs from BMC Bioinformatics
#2,614
of 2,790 outputs
Outputs of similar age
#125,314
of 151,945 outputs
Outputs of similar age from BMC Bioinformatics
#119
of 123 outputs
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