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Determining significance of pairwise co-occurrences of events in bursty sequences

Overview of attention for article published in BMC Bioinformatics, August 2008
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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1 Q&A thread

Citations

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16 Dimensions

Readers on

mendeley
33 Mendeley
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2 CiteULike
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Title
Determining significance of pairwise co-occurrences of events in bursty sequences
Published in
BMC Bioinformatics, August 2008
DOI 10.1186/1471-2105-9-336
Pubmed ID
Authors

Niina Haiminen, Heikki Mannila, Evimaria Terzi

Abstract

Event sequences where different types of events often occur close together arise, e.g., when studying potential transcription factor binding sites (TFBS, events) of certain transcription factors (TF, types) in a DNA sequence. These events tend to occur in bursts: in some genomic regions there are more genes and therefore potentially more binding sites, while in some, possibly very long regions, hardly any events occur. Also some types of events may occur in the sequence more often than others. Tendencies of co-occurrence of binding sites of two or more TFs are interesting, as they may imply a co-operative role between the TFs in regulatory processes. Determining a numerical value to summarize the tendency for co-occurrence between two TFs can be done in a number of ways. However, testing for the significance of such values should be done with respect to a relevant null model that takes into account the global sequence structure.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
France 1 3%
Germany 1 3%
Spain 1 3%
United Kingdom 1 3%
Unknown 27 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 30%
Student > Ph. D. Student 10 30%
Student > Doctoral Student 3 9%
Professor > Associate Professor 3 9%
Other 2 6%
Other 4 12%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 48%
Computer Science 7 21%
Biochemistry, Genetics and Molecular Biology 3 9%
Engineering 3 9%
Neuroscience 1 3%
Other 1 3%
Unknown 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 11 February 2011.
All research outputs
#1,402,274
of 3,631,524 outputs
Outputs from BMC Bioinformatics
#1,008
of 2,289 outputs
Outputs of similar age
#15,246
of 55,792 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 80 outputs
Altmetric has tracked 3,631,524 research outputs across all sources so far. This one has received more attention than most of these and is in the 60th percentile.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 52% 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 55,792 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 80 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 57% of its contemporaries.