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zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm

Overview of attention for article published in BMC Bioinformatics, November 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
21 Mendeley
citeulike
3 CiteULike
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Title
zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm
Published in
BMC Bioinformatics, November 2013
DOI 10.1186/1471-2105-14-339
Pubmed ID
Authors

Andreas Sand, Martin Kristiansen, Christian NS Pedersen, Thomas Mailund

Abstract

Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 10%
United States 1 5%
Unknown 18 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 24%
Student > Ph. D. Student 4 19%
Student > Master 4 19%
Librarian 1 5%
Student > Bachelor 1 5%
Other 3 14%
Unknown 3 14%
Readers by discipline Count As %
Computer Science 8 38%
Agricultural and Biological Sciences 5 24%
Biochemistry, Genetics and Molecular Biology 3 14%
Social Sciences 2 10%
Environmental Science 1 5%
Other 0 0%
Unknown 2 10%

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 26 November 2013.
All research outputs
#4,115,370
of 15,308,819 outputs
Outputs from BMC Bioinformatics
#1,757
of 5,598 outputs
Outputs of similar age
#63,856
of 259,507 outputs
Outputs of similar age from BMC Bioinformatics
#147
of 424 outputs
Altmetric has tracked 15,308,819 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 5,598 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 gotten more attention than average, scoring higher than 68% 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 259,507 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 75% of its contemporaries.
We're also able to compare this research output to 424 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 65% of its contemporaries.