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Fast MCMC sampling for hidden markov models to determine copy number variations

Overview of attention for article published in BMC Bioinformatics, November 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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2 tweeters
patent
12 patents

Citations

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6 Dimensions

Readers on

mendeley
38 Mendeley
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3 CiteULike
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Title
Fast MCMC sampling for hidden markov models to determine copy number variations
Published in
BMC Bioinformatics, November 2011
DOI 10.1186/1471-2105-12-428
Pubmed ID
Authors

Md Pavel Mahmud, Alexander Schliep

Abstract

Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC) sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times; datasets coming from high-density arrays and next generation sequencing amplify these problems.

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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 13%
Germany 1 3%
Unknown 32 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 39%
Student > Ph. D. Student 10 26%
Student > Master 3 8%
Student > Postgraduate 2 5%
Professor > Associate Professor 2 5%
Other 3 8%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 39%
Computer Science 7 18%
Engineering 4 11%
Mathematics 2 5%
Business, Management and Accounting 2 5%
Other 6 16%
Unknown 2 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 10 December 2019.
All research outputs
#1,678,952
of 15,184,505 outputs
Outputs from BMC Bioinformatics
#636
of 5,572 outputs
Outputs of similar age
#12,191
of 114,475 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 177 outputs
Altmetric has tracked 15,184,505 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,572 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 88% 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 114,475 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 89% of its contemporaries.
We're also able to compare this research output to 177 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.