| Title |
De novo Nanopore read quality improvement using deep learning
|
|---|---|
| Published in |
BMC Bioinformatics, November 2019
|
| DOI | 10.1186/s12859-019-3103-z |
| Pubmed ID | |
| Authors |
Nathan LaPierre, Rob Egan, Wei Wang, Zhong Wang |
X Demographics
The data shown below were collected from the profiles of 22 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 | 5 | 23% |
| United Kingdom | 3 | 14% |
| Netherlands | 2 | 9% |
| Spain | 1 | 5% |
| Germany | 1 | 5% |
| Korea, Democratic People's Republic of | 1 | 5% |
| Brazil | 1 | 5% |
| France | 1 | 5% |
| Unknown | 7 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 55% |
| Scientists | 10 | 45% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 79 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 20% |
| Researcher | 13 | 16% |
| Student > Master | 10 | 13% |
| Student > Bachelor | 6 | 8% |
| Other | 5 | 6% |
| Other | 9 | 11% |
| Unknown | 20 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 18 | 23% |
| Agricultural and Biological Sciences | 15 | 19% |
| Computer Science | 7 | 9% |
| Nursing and Health Professions | 3 | 4% |
| Medicine and Dentistry | 3 | 4% |
| Other | 9 | 11% |
| Unknown | 24 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 18 October 2021.
All research outputs
#3,497,784
of 25,827,956 outputs
Outputs from BMC Bioinformatics
#1,126
of 7,754 outputs
Outputs of similar age
#71,192
of 383,155 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 165 outputs
Altmetric has tracked 25,827,956 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,754 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 85% 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 383,155 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 81% of its contemporaries.
We're also able to compare this research output to 165 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.