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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile
|
|---|---|
| Published in |
Frontiers in immunology, July 2020
|
| DOI | 10.3389/fimmu.2020.01470 |
| Pubmed ID | |
| Authors |
Olivia Estévez, Luis Anibarro, Elina Garet, Ángeles Pallares, Laura Barcia, Laura Calviño, Cremildo Maueia, Tufária Mussá, Florentino Fdez-Riverola, Daniel Glez-Peña, Miguel Reboiro-Jato, Hugo López-Fernández, Nuno A. Fonseca, Rajko Reljic, África González-Fernández |
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 % |
|---|---|---|
| Spain | 3 | 27% |
| United Kingdom | 2 | 18% |
| Unknown | 6 | 55% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 73% |
| Scientists | 3 | 27% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 17% |
| Student > Master | 8 | 17% |
| Student > Ph. D. Student | 5 | 11% |
| Other | 4 | 9% |
| Student > Postgraduate | 3 | 6% |
| Other | 6 | 13% |
| Unknown | 13 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 15% |
| Biochemistry, Genetics and Molecular Biology | 7 | 15% |
| Agricultural and Biological Sciences | 3 | 6% |
| Immunology and Microbiology | 3 | 6% |
| Computer Science | 2 | 4% |
| Other | 11 | 23% |
| Unknown | 14 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 19 August 2020.
All research outputs
#5,278,766
of 25,628,260 outputs
Outputs from Frontiers in immunology
#5,771
of 32,065 outputs
Outputs of similar age
#123,780
of 430,895 outputs
Outputs of similar age from Frontiers in immunology
#216
of 772 outputs
Altmetric has tracked 25,628,260 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 32,065 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has done well, scoring higher than 81% 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 430,895 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 772 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 71% of its contemporaries.