The data shown below were collected from the profiles of 38 X users who shared this research output. Click here to find out more about how the information was compiled.
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| Title |
A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data
|
|---|---|
| Published in |
Nature Machine Intelligence, January 2021
|
| DOI | 10.1038/s42256-020-00276-w |
| Pubmed ID | |
| Authors |
Ruoqi Liu, Lai Wei, Ping Zhang |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 24% |
| Japan | 2 | 5% |
| United Kingdom | 2 | 5% |
| Netherlands | 2 | 5% |
| Australia | 2 | 5% |
| Italy | 1 | 3% |
| Canada | 1 | 3% |
| Pakistan | 1 | 3% |
| India | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 14 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 20 | 53% |
| Scientists | 13 | 34% |
| Practitioners (doctors, other healthcare professionals) | 4 | 11% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 159 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 36 | 23% |
| Student > Ph. D. Student | 29 | 18% |
| Student > Master | 10 | 6% |
| Student > Bachelor | 8 | 5% |
| Student > Doctoral Student | 6 | 4% |
| Other | 15 | 9% |
| Unknown | 55 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 15 | 9% |
| Biochemistry, Genetics and Molecular Biology | 14 | 9% |
| Medicine and Dentistry | 12 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 5% |
| Engineering | 8 | 5% |
| Other | 41 | 26% |
| Unknown | 61 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 237. 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 06 December 2021.
All research outputs
#172,912
of 26,779,733 outputs
Outputs from Nature Machine Intelligence
#45
of 851 outputs
Outputs of similar age
#4,889
of 536,085 outputs
Outputs of similar age from Nature Machine Intelligence
#1
of 37 outputs
Altmetric has tracked 26,779,733 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 851 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 62.8. This one has done particularly well, scoring higher than 94% 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 536,085 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 99% of its contemporaries.
We're also able to compare this research output to 37 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 97% of its contemporaries.