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MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification

Overview of attention for article published in BMC Bioinformatics, December 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
13 tweeters
facebook
2 Facebook pages

Citations

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16 Dimensions

Readers on

mendeley
32 Mendeley
citeulike
1 CiteULike
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Title
MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0401-3
Pubmed ID
Authors

Jason M Knight, Ivan Ivanov, Edward R Dougherty

Abstract

BackgroundSequencing datasets consist of a finite number of reads which map to specific regions of a reference genome. Most effort in modeling these datasets focuses on the detection of univariate differentially expressed genes. However, for classification, we must consider multiple genes and their interactions.ResultsThus, we introduce a hierarchical multivariate Poisson model (MP) and the associated optimal Bayesian classifier (OBC) for classifying samples using sequencing data. Lacking closed-form solutions, we employ a Monte Carlo Markov Chain (MCMC) approach to perform classification. We demonstrate superior or equivalent classification performance compared to typical classifiers for two synthetic datasets and over a range of classification problem difficulties. We also introduce the Bayesian minimum mean squared error (MMSE) conditional error estimator and demonstrate its computation over the feature space. In addition, we demonstrate superior or leading class performance over an RNA-Seq dataset containing two lung cancer tumor types from The Cancer Genome Atlas (TCGA).ConclusionsThrough model-based, optimal Bayesian classification, we demonstrate superior classification performance for both synthetic and real RNA-Seq datasets. A tutorial video and Python source code is available under an open source license at http://bit.ly/1gimnss.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
Malaysia 1 3%
Unknown 29 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 28%
Researcher 6 19%
Unspecified 5 16%
Professor > Associate Professor 4 13%
Student > Master 3 9%
Other 5 16%
Readers by discipline Count As %
Computer Science 8 25%
Unspecified 7 22%
Agricultural and Biological Sciences 6 19%
Biochemistry, Genetics and Molecular Biology 5 16%
Medicine and Dentistry 4 13%
Other 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 15 September 2015.
All research outputs
#3,273,408
of 13,426,363 outputs
Outputs from BMC Bioinformatics
#1,369
of 4,990 outputs
Outputs of similar age
#63,795
of 296,511 outputs
Outputs of similar age from BMC Bioinformatics
#89
of 317 outputs
Altmetric has tracked 13,426,363 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,990 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 296,511 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 317 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.