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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving MEME via a two-tiered significance analysis
|
|---|---|
| Published in |
Bioinformatics, March 2014
|
| DOI | 10.1093/bioinformatics/btu163 |
| Pubmed ID | |
| Authors |
Emi Tanaka, Timothy L Bailey, Uri Keich |
| Abstract |
With over 9000 unique users recorded in the first half of 2013, MEME is one of the most popular motif-finding tools available. Reliable estimates of the statistical significance of motifs can greatly increase the usefulness of any motif finder. By analogy, it is difficult to imagine evaluating a BLAST result without its accompanying E-value. Currently MEME evaluates its EM-generated candidate motifs using an extension of BLAST's E-value to the motif-finding context. Although we previously indicated the drawbacks of MEME's current significance evaluation, we did not offer a practical substitute suited for its needs, especially because MEME also relies on the E-value internally to rank competing candidate motifs. |
X Demographics
The data shown below were collected from the profiles of 6 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 | 2 | 33% |
| Norway | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 83% |
| Members of the public | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 7% |
| France | 2 | 4% |
| Japan | 1 | 2% |
| Norway | 1 | 2% |
| Unknown | 38 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 27% |
| Student > Ph. D. Student | 10 | 22% |
| Student > Master | 5 | 11% |
| Student > Bachelor | 4 | 9% |
| Professor | 4 | 9% |
| Other | 6 | 13% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 22 | 49% |
| Biochemistry, Genetics and Molecular Biology | 10 | 22% |
| Computer Science | 5 | 11% |
| Mathematics | 1 | 2% |
| Arts and Humanities | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 5 | 11% |
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 03 July 2014.
All research outputs
#14,599,159
of 25,371,288 outputs
Outputs from Bioinformatics
#8,581
of 12,808 outputs
Outputs of similar age
#114,319
of 236,976 outputs
Outputs of similar age from Bioinformatics
#100
of 167 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 30th percentile – i.e., 30% 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 236,976 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 51% of its contemporaries.
We're also able to compare this research output to 167 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.