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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An algorithm of discovering signatures from DNA databases on a computer cluster
|
|---|---|
| Published in |
BMC Bioinformatics, October 2014
|
| DOI | 10.1186/1471-2105-15-339 |
| Pubmed ID | |
| Authors |
Hsiao Ping Lee, Tzu-Fang Sheu |
| Abstract |
Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved. |
X Demographics
The data shown below were collected from the profiles of 16 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 | 8 | 50% |
| India | 1 | 6% |
| Spain | 1 | 6% |
| Unknown | 6 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 50% |
| Scientists | 7 | 44% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Finland | 1 | 7% |
| Unknown | 14 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 20% |
| Researcher | 3 | 20% |
| Professor > Associate Professor | 3 | 20% |
| Student > Bachelor | 2 | 13% |
| Student > Master | 1 | 7% |
| Other | 3 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 40% |
| Agricultural and Biological Sciences | 4 | 27% |
| Biochemistry, Genetics and Molecular Biology | 4 | 27% |
| Unspecified | 1 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 10 November 2014.
All research outputs
#2,871,203
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#924
of 7,454 outputs
Outputs of similar age
#32,583
of 256,722 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 112 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 87% 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 256,722 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 112 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.