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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ParticleCall: A particle filter for base calling in next-generation sequencing systems
|
|---|---|
| Published in |
BMC Bioinformatics, July 2012
|
| DOI | 10.1186/1471-2105-13-160 |
| Pubmed ID | |
| Authors |
Xiaohu Shen, Haris Vikalo |
| Abstract |
Next-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, thus enabling routine sequencing tasks and taking us one step closer to personalized medicine. Accuracy and lengths of their reads, however, are yet to surpass those provided by the conventional Sanger sequencing method. This motivates the search for computationally efficient algorithms capable of reliable and accurate detection of the order of nucleotides in short DNA fragments from the acquired data. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 3% |
| United States | 1 | 3% |
| China | 1 | 3% |
| Unknown | 27 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 37% |
| Student > Ph. D. Student | 6 | 20% |
| Student > Postgraduate | 3 | 10% |
| Professor | 2 | 7% |
| Student > Master | 2 | 7% |
| Other | 4 | 13% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 30% |
| Computer Science | 4 | 13% |
| Mathematics | 3 | 10% |
| Medicine and Dentistry | 2 | 7% |
| Economics, Econometrics and Finance | 2 | 7% |
| Other | 7 | 23% |
| Unknown | 3 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 July 2012.
All research outputs
#20,160,460
of 22,669,724 outputs
Outputs from BMC Bioinformatics
#6,820
of 7,247 outputs
Outputs of similar age
#148,259
of 164,608 outputs
Outputs of similar age from BMC Bioinformatics
#84
of 91 outputs
Altmetric has tracked 22,669,724 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 1st percentile – i.e., 1% 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 164,608 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.