| Title |
MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease
|
|---|---|
| Published in |
Cell Systems, May 2019
|
| DOI | 10.1016/j.cels.2019.04.003 |
| Pubmed ID | |
| Authors |
Jaclyn N. Taroni, Peter C. Grayson, Qiwen Hu, Sean Eddy, Matthias Kretzler, Peter A. Merkel, Casey S. Greene |
X Demographics
The data shown below were collected from the profiles of 79 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 | 34 | 43% |
| Canada | 2 | 3% |
| Germany | 2 | 3% |
| United Kingdom | 2 | 3% |
| Ecuador | 1 | 1% |
| Korea, Republic of | 1 | 1% |
| Austria | 1 | 1% |
| Guinea | 1 | 1% |
| Denmark | 1 | 1% |
| Other | 7 | 9% |
| Unknown | 27 | 34% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 41 | 52% |
| Members of the public | 36 | 46% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
| Practitioners (doctors, other healthcare professionals) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 214 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 214 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 43 | 20% |
| Student > Ph. D. Student | 39 | 18% |
| Student > Doctoral Student | 19 | 9% |
| Student > Master | 19 | 9% |
| Student > Bachelor | 17 | 8% |
| Other | 28 | 13% |
| Unknown | 49 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 54 | 25% |
| Computer Science | 31 | 14% |
| Agricultural and Biological Sciences | 29 | 14% |
| Medicine and Dentistry | 16 | 7% |
| Engineering | 10 | 5% |
| Other | 25 | 12% |
| Unknown | 49 | 23% |
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 07 March 2024.
All research outputs
#905,673
of 25,605,018 outputs
Outputs from Cell Systems
#200
of 988 outputs
Outputs of similar age
#20,190
of 363,939 outputs
Outputs of similar age from Cell Systems
#9
of 31 outputs
Altmetric has tracked 25,605,018 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 988 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 37.1. This one has done well, scoring higher than 79% 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 363,939 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 94% of its contemporaries.
We're also able to compare this research output to 31 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 74% of its contemporaries.