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Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

Overview of attention for article published in BMC Bioinformatics, November 2012
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2 tweeters

Citations

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18 Dimensions

Readers on

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50 Mendeley
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3 CiteULike
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Title
Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects
Published in
BMC Bioinformatics, November 2012
DOI 10.1186/1471-2105-13-300
Pubmed ID
Authors

Kui Wang, Shu Kay Ng, Geoffrey J McLachlan

Abstract

Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
Brazil 1 2%
South Africa 1 2%
Russia 1 2%
Japan 1 2%
United States 1 2%
Unknown 44 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 30%
Researcher 13 26%
Professor > Associate Professor 7 14%
Student > Master 4 8%
Student > Doctoral Student 2 4%
Other 7 14%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 40%
Computer Science 8 16%
Mathematics 7 14%
Biochemistry, Genetics and Molecular Biology 5 10%
Engineering 4 8%
Other 3 6%
Unknown 3 6%

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 22 November 2012.
All research outputs
#9,508,795
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,587
of 4,588 outputs
Outputs of similar age
#90,199
of 135,733 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 34 outputs
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