| Title |
Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks
|
|---|---|
| Published in |
BMC Bioinformatics, June 2019
|
| DOI | 10.1186/s12859-019-2939-6 |
| Pubmed ID | |
| Authors |
Jiesi Luo, Wei Chen, Li Xue, Bin Tang |
X Demographics
The data shown below were collected from the profiles of 29 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 | 7 | 24% |
| India | 2 | 7% |
| France | 1 | 3% |
| Nepal | 1 | 3% |
| Sri Lanka | 1 | 3% |
| Netherlands | 1 | 3% |
| Unknown | 16 | 55% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 23 | 79% |
| Scientists | 4 | 14% |
| Practitioners (doctors, other healthcare professionals) | 2 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 65 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 10 | 15% |
| Student > Ph. D. Student | 9 | 14% |
| Researcher | 9 | 14% |
| Other | 5 | 8% |
| Student > Master | 5 | 8% |
| Other | 11 | 17% |
| Unknown | 16 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 16 | 25% |
| Agricultural and Biological Sciences | 10 | 15% |
| Computer Science | 6 | 9% |
| Engineering | 4 | 6% |
| Medicine and Dentistry | 2 | 3% |
| Other | 10 | 15% |
| Unknown | 17 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 11 April 2023.
All research outputs
#2,483,663
of 25,809,966 outputs
Outputs from BMC Bioinformatics
#608
of 7,764 outputs
Outputs of similar age
#51,390
of 370,631 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 186 outputs
Altmetric has tracked 25,809,966 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,764 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 particularly well, scoring higher than 92% 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 370,631 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 86% of its contemporaries.
We're also able to compare this research output to 186 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.