| Title |
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction
|
|---|---|
| Published in |
Nature Communications, September 2023
|
| DOI | 10.1038/s41467-023-41303-9 |
| Pubmed ID | |
| Authors |
Yang Li, Chengxin Zhang, Chenjie Feng, Robin Pearce, P. Lydia Freddolino, Yang Zhang |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Poland | 1 | 13% |
| Switzerland | 1 | 13% |
| United States | 1 | 13% |
| Unknown | 5 | 63% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 3 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 40 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 15% |
| Student > Ph. D. Student | 3 | 8% |
| Student > Doctoral Student | 2 | 5% |
| Student > Bachelor | 2 | 5% |
| Student > Master | 2 | 5% |
| Other | 2 | 5% |
| Unknown | 23 | 57% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 18% |
| Computer Science | 2 | 5% |
| Chemistry | 2 | 5% |
| Physics and Astronomy | 2 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 4 | 10% |
| Unknown | 22 | 55% |
Attention Score in Context
This research output has an Altmetric Attention Score of 47. 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 25 December 2023.
All research outputs
#876,774
of 25,055,009 outputs
Outputs from Nature Communications
#14,573
of 55,151 outputs
Outputs of similar age
#13,823
of 316,238 outputs
Outputs of similar age from Nature Communications
#410
of 2,231 outputs
Altmetric has tracked 25,055,009 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 55,151 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 73% 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 316,238 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 95% of its contemporaries.
We're also able to compare this research output to 2,231 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.