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Twitter Demographics
Mendeley readers
Attention Score in Context
| Title |
MCOIN: a novel heuristic for determining transcription factor binding site motif width
|
|---|---|
| Published in |
Algorithms for Molecular Biology, June 2013
|
| DOI | 10.1186/1748-7188-8-16 |
| Pubmed ID | |
| Authors |
Alastair M Kilpatrick, Bruce Ward, Stuart Aitken |
| Abstract |
In transcription factor binding site discovery, the true width of the motif to be discovered is generally not known a priori. The ability to compute the most likely width of a motif is therefore a highly desirable property for motif discovery algorithms. However, this is a challenging computational problem as a result of changing model dimensionality at changing motif widths. The complexity of the problem is increased as the discovered model at the true motif width need not be the most statistically significant in a set of candidate motif models. Further, the core motif discovery algorithm used cannot guarantee to return the best possible result at each candidate width. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 33% |
| Unknown | 2 | 67% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 1 | 33% |
| Unknown | 2 | 67% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 29 January 2014.
All research outputs
#14,171,982
of 22,713,403 outputs
Outputs from Algorithms for Molecular Biology
#111
of 264 outputs
Outputs of similar age
#110,686
of 196,330 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 2 outputs
Altmetric has tracked 22,713,403 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% 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 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 196,330 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them