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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Monte Carlo-based framework enhances the discovery and interpretation of regulatory sequence motifs
|
|---|---|
| Published in |
BMC Bioinformatics, November 2012
|
| DOI | 10.1186/1471-2105-13-317 |
| Pubmed ID | |
| Authors |
Phillip Seitzer, Elizabeth G Wilbanks, David J Larsen, Marc T Facciotti |
| Abstract |
Discovery of functionally significant short, statistically overrepresented subsequence patterns (motifs) in a set of sequences is a challenging problem in bioinformatics. Oftentimes, not all sequences in the set contain a motif. These non-motif-containing sequences complicate the algorithmic discovery of motifs. Filtering the non-motif-containing sequences from the larger set of sequences while simultaneously determining the identity of the motif is, therefore, desirable and a non-trivial problem in motif discovery research. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| France | 1 | 2% |
| Sri Lanka | 1 | 2% |
| Sweden | 1 | 2% |
| Russia | 1 | 2% |
| Argentina | 1 | 2% |
| Unknown | 55 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 20 | 32% |
| Student > Ph. D. Student | 12 | 19% |
| Student > Master | 8 | 13% |
| Student > Bachelor | 5 | 8% |
| Professor > Associate Professor | 4 | 6% |
| Other | 9 | 15% |
| Unknown | 4 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 48% |
| Biochemistry, Genetics and Molecular Biology | 10 | 16% |
| Computer Science | 7 | 11% |
| Engineering | 4 | 6% |
| Mathematics | 1 | 2% |
| Other | 5 | 8% |
| Unknown | 5 | 8% |
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 18 January 2013.
All research outputs
#7,696,936
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#3,050
of 7,418 outputs
Outputs of similar age
#83,396
of 281,582 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 103 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 58% 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 281,582 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 70% of its contemporaries.
We're also able to compare this research output to 103 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 55% of its contemporaries.