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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A computational method for detecting copy number variations using scale-space filtering
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-57 |
| Pubmed ID | |
| Authors |
Jongkeun Lee, Unjoo Lee, Baeksop Kim, Jeehee Yoon |
| Abstract |
As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data. |
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 % |
|---|---|---|
| Germany | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 7% |
| France | 1 | 4% |
| Switzerland | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 23 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 32% |
| Student > Ph. D. Student | 7 | 25% |
| Student > Master | 4 | 14% |
| Student > Doctoral Student | 2 | 7% |
| Other | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 61% |
| Computer Science | 7 | 25% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Immunology and Microbiology | 1 | 4% |
| Unknown | 2 | 7% |
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 21 February 2013.
All research outputs
#13,882,821
of 22,696,971 outputs
Outputs from BMC Bioinformatics
#4,469
of 7,254 outputs
Outputs of similar age
#107,769
of 192,548 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 138 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% 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 192,548 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.