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Jaccard index based similarity measure to compare transcription factor binding site models

Overview of attention for article published in Algorithms for Molecular Biology, September 2013
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Mentioned by

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2 tweeters

Citations

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29 Dimensions

Readers on

mendeley
63 Mendeley
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2 CiteULike
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Title
Jaccard index based similarity measure to compare transcription factor binding site models
Published in
Algorithms for Molecular Biology, September 2013
DOI 10.1186/1748-7188-8-23
Pubmed ID
Abstract

Positional weight matrix (PWM) remains the most popular for quantification of transcription factor (TF) binding. PWM supplied with a score threshold defines a set of putative transcription factor binding sites (TFBS), thus providing a TFBS model.TF binding DNA fragments obtained by different experimental methods usually give similar but not identical PWMs. This is also common for different TFs from the same structural family. Thus it is often necessary to measure the similarity between PWMs. The popular tools compare PWMs directly using matrix elements. Yet, for log-odds PWMs, negative elements do not contribute to the scores of highly scoring TFBS and thus may be different without affecting the sets of the best recognized binding sites. Moreover, the two TFBS sets recognized by a given pair of PWMs can be more or less different depending on the score thresholds.

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

Geographical breakdown

Country Count As %
France 3 5%
Japan 1 2%
Unknown 59 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 30%
Student > Ph. D. Student 16 25%
Student > Bachelor 11 17%
Researcher 6 10%
Professor 5 8%
Other 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 37%
Computer Science 19 30%
Biochemistry, Genetics and Molecular Biology 9 14%
Engineering 3 5%
Mathematics 1 2%
Other 5 8%
Unknown 3 5%

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 October 2013.
All research outputs
#8,687,837
of 11,293,566 outputs
Outputs from Algorithms for Molecular Biology
#106
of 177 outputs
Outputs of similar age
#95,275
of 152,779 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 5 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 177 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 152,779 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.