| Title |
Transcriptomic signatures and repurposing drugs for COVID-19 patients: findings of bioinformatics analyses
|
|---|---|
| Published in |
Computational and Structural Biotechnology Journal, December 2020
|
| DOI | 10.1016/j.csbj.2020.11.056 |
| Pubmed ID | |
| Authors |
Guobing Li, Shasha Ruan, Xiaolu Zhao, Qi Liu, Yali Dou, Fengbiao Mao |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 9% |
| Indonesia | 1 | 9% |
| Germany | 1 | 9% |
| United States | 1 | 9% |
| France | 1 | 9% |
| Norway | 1 | 9% |
| Unknown | 5 | 45% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 64% |
| Scientists | 2 | 18% |
| Practitioners (doctors, other healthcare professionals) | 1 | 9% |
| Science communicators (journalists, bloggers, editors) | 1 | 9% |
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 > Bachelor | 24 | 15% |
| Student > Master | 16 | 10% |
| Researcher | 13 | 8% |
| Student > Ph. D. Student | 12 | 7% |
| Student > Doctoral Student | 11 | 7% |
| Other | 18 | 11% |
| Unknown | 67 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 30 | 19% |
| Biochemistry, Genetics and Molecular Biology | 18 | 11% |
| Nursing and Health Professions | 9 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 5% |
| Computer Science | 3 | 2% |
| Other | 19 | 12% |
| Unknown | 74 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 26 September 2021.
All research outputs
#2,298,809
of 25,387,668 outputs
Outputs from Computational and Structural Biotechnology Journal
#135
of 2,363 outputs
Outputs of similar age
#60,984
of 518,419 outputs
Outputs of similar age from Computational and Structural Biotechnology Journal
#11
of 153 outputs
Altmetric has tracked 25,387,668 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 2,363 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 518,419 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 88% of its contemporaries.
We're also able to compare this research output to 153 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 92% of its contemporaries.