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Analysis of pattern overlaps and exact computation of P-values of pattern occurrences numbers: case of Hidden Markov Models

Overview of attention for article published in Algorithms for Molecular Biology, December 2014
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Title
Analysis of pattern overlaps and exact computation of P-values of pattern occurrences numbers: case of Hidden Markov Models
Published in
Algorithms for Molecular Biology, December 2014
DOI 10.1186/s13015-014-0025-1
Pubmed ID
Authors

Mireille Régnier, Evgenia Furletova, Victor Yakovlev, Mikhail Roytberg

Abstract

Finding new functional fragments in biological sequences is a challenging problem. Methods addressing this problem commonly search for clusters of pattern occurrences that are statistically significant. A measure of statistical significance is the P-value of a number of pattern occurrences, i.e. the probability to find at least S occurrences of words from a pattern in a random text of length N generated according to a given probability model. All words of the pattern are supposed to be of same length.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 33%
Student > Ph. D. Student 3 20%
Student > Master 2 13%
Student > Bachelor 1 7%
Other 1 7%
Other 2 13%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 27%
Agricultural and Biological Sciences 4 27%
Computer Science 2 13%
Mathematics 1 7%
Physics and Astronomy 1 7%
Other 1 7%
Unknown 2 13%
Attention Score in Context

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 23 December 2014.
All research outputs
#20,247,117
of 22,775,504 outputs
Outputs from Algorithms for Molecular Biology
#233
of 264 outputs
Outputs of similar age
#297,033
of 354,383 outputs
Outputs of similar age from Algorithms for Molecular Biology
#7
of 10 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.