| Title |
A deep learning framework to predict binding preference of RNA constituents on protein surface
|
|---|---|
| Published in |
Nature Communications, October 2019
|
| DOI | 10.1038/s41467-019-12920-0 |
| Pubmed ID | |
| Authors |
Jordy Homing Lam, Yu Li, Lizhe Zhu, Ramzan Umarov, Hanlun Jiang, Amélie Héliou, Fu Kit Sheong, Tianyun Liu, Yongkang Long, Yunfei Li, Liang Fang, Russ B. Altman, Wei Chen, Xuhui Huang, Xin Gao |
X Demographics
The data shown below were collected from the profiles of 23 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 | 22% |
| Czechia | 3 | 13% |
| China | 1 | 4% |
| Switzerland | 1 | 4% |
| India | 1 | 4% |
| Japan | 1 | 4% |
| Sweden | 1 | 4% |
| Unknown | 10 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 61% |
| Scientists | 8 | 35% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 161 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 35 | 22% |
| Researcher | 22 | 14% |
| Student > Master | 13 | 8% |
| Student > Bachelor | 9 | 6% |
| Student > Doctoral Student | 8 | 5% |
| Other | 24 | 15% |
| Unknown | 50 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 44 | 27% |
| Agricultural and Biological Sciences | 13 | 8% |
| Computer Science | 12 | 7% |
| Chemistry | 11 | 7% |
| Physics and Astronomy | 3 | 2% |
| Other | 20 | 12% |
| Unknown | 58 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 36. 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 23 January 2020.
All research outputs
#1,105,699
of 25,079,481 outputs
Outputs from Nature Communications
#17,418
of 55,165 outputs
Outputs of similar age
#24,309
of 370,064 outputs
Outputs of similar age from Nature Communications
#477
of 1,458 outputs
Altmetric has tracked 25,079,481 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 55,165 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.9. This one has gotten more attention than average, scoring higher than 68% 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 370,064 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 1,458 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.