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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ADaM: augmenting existing approximate fast matching algorithms with efficient and exact range queries
|
|---|---|
| Published in |
BMC Bioinformatics, May 2014
|
| DOI | 10.1186/1471-2105-15-s7-s1 |
| Pubmed ID | |
| Authors |
Nathan L Clement, Lee P Thompson, Daniel P Miranker |
| Abstract |
Drug discovery, disease detection, and personalized medicine are fast-growing areas of genomic research. With the advancement of next-generation sequencing techniques, researchers can obtain an abundance of data for many different biological assays in a short period of time. When this data is error-free, the result is a high-quality base-pair resolution picture of the genome. However, when the data is lossy the heuristic algorithms currently used when aligning next-generation sequences causes the corresponding accuracy to drop. |
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 % |
|---|---|---|
| Israel | 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 4 | 40% |
| Student > Ph. D. Student | 3 | 30% |
| Researcher | 1 | 10% |
| Professor | 1 | 10% |
| Unknown | 1 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 40% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Computer Science | 1 | 10% |
| Medicine and Dentistry | 1 | 10% |
| Neuroscience | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 1 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 August 2017.
All research outputs
#6,780,313
of 22,765,347 outputs
Outputs from BMC Bioinformatics
#2,580
of 7,273 outputs
Outputs of similar age
#64,640
of 226,687 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 153 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,273 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 63% 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 226,687 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 71% of its contemporaries.
We're also able to compare this research output to 153 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 67% of its contemporaries.