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ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

Overview of attention for article published in PLoS Computational Biology, October 2013
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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 (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

blogs
1 blog
twitter
7 X users
googleplus
1 Google+ user

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
42 Mendeley
citeulike
3 CiteULike
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Title
ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003234
Pubmed ID
Authors

André Yoshiaki Kashiwabara, Ígor Bonadio, Vitor Onuchic, Felipe Amado, Rafael Mathias, Alan Mitchell Durham

Abstract

Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 5%
Unknown 40 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 29%
Student > Ph. D. Student 7 17%
Student > Bachelor 5 12%
Student > Master 5 12%
Student > Doctoral Student 3 7%
Other 9 21%
Unknown 1 2%
Readers by discipline Count As %
Computer Science 15 36%
Agricultural and Biological Sciences 13 31%
Biochemistry, Genetics and Molecular Biology 8 19%
Physics and Astronomy 1 2%
Sports and Recreations 1 2%
Other 0 0%
Unknown 4 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 October 2013.
All research outputs
#2,956,581
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#2,607
of 8,964 outputs
Outputs of similar age
#26,166
of 220,322 outputs
Outputs of similar age from PLoS Computational Biology
#34
of 135 outputs
Altmetric has tracked 25,394,764 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 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 70% 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 220,322 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 88% of its contemporaries.
We're also able to compare this research output to 135 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 74% of its contemporaries.