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Mendeley readers
Attention Score in Context
| 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 | |
| Authors |
Ilya E Vorontsov, Ivan V Kulakovskiy, Vsevolod J Makeev |
| 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 3 | 3% |
| Japan | 1 | 1% |
| Unknown | 86 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 22 | 24% |
| Student > Ph. D. Student | 20 | 22% |
| Student > Bachelor | 15 | 17% |
| Researcher | 7 | 8% |
| Professor | 6 | 7% |
| Other | 10 | 11% |
| Unknown | 10 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 27% |
| Computer Science | 21 | 23% |
| Biochemistry, Genetics and Molecular Biology | 16 | 18% |
| Engineering | 5 | 6% |
| Decision Sciences | 2 | 2% |
| Other | 9 | 10% |
| Unknown | 13 | 14% |
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
#15,281,593
of 22,725,280 outputs
Outputs from Algorithms for Molecular Biology
#148
of 264 outputs
Outputs of similar age
#126,814
of 205,845 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 5 outputs
Altmetric has tracked 22,725,280 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% 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 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 205,845 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% 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.