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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A novel method for predicting cell abundance based on single-cell RNA-seq data
|
|---|---|
| Published in |
BMC Bioinformatics, August 2021
|
| DOI | 10.1186/s12859-021-04187-4 |
| Pubmed ID | |
| Authors |
Jiajie Peng, Lu Han, Xuequn Shang |
X Demographics
The data shown below were collected from the profiles of 19 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 | 6 | 32% |
| Australia | 1 | 5% |
| Korea, Republic of | 1 | 5% |
| Spain | 1 | 5% |
| India | 1 | 5% |
| Japan | 1 | 5% |
| Italy | 1 | 5% |
| Sweden | 1 | 5% |
| United Kingdom | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 5 | 26% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 53% |
| Members of the public | 9 | 47% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 17% |
| Student > Bachelor | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Student > Master | 1 | 8% |
| Unknown | 7 | 58% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 17% |
| Mathematics | 1 | 8% |
| Neuroscience | 1 | 8% |
| Engineering | 1 | 8% |
| Unknown | 7 | 58% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 05 January 2022.
All research outputs
#4,094,663
of 25,235,161 outputs
Outputs from BMC Bioinformatics
#1,369
of 7,661 outputs
Outputs of similar age
#84,423
of 423,987 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 120 outputs
Altmetric has tracked 25,235,161 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,661 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 well, scoring higher than 82% 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 423,987 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 80% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.