| Title |
Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
|
|---|---|
| Published in |
Nature Communications, June 2021
|
| DOI | 10.1038/s41467-021-23713-9 |
| Pubmed ID | |
| Authors |
Mathias Wilhelm, Daniel P. Zolg, Michael Graber, Siegfried Gessulat, Tobias Schmidt, Karsten Schnatbaum, Celina Schwencke-Westphal, Philipp Seifert, Niklas de Andrade Krätzig, Johannes Zerweck, Tobias Knaute, Eva Bräunlein, Patroklos Samaras, Ludwig Lautenbacher, Susan Klaeger, Holger Wenschuh, Roland Rad, Bernard Delanghe, Andreas Huhmer, Steven A. Carr, Karl R. Clauser, Angela M. Krackhardt, Ulf Reimer, Bernhard Kuster |
X Demographics
The data shown below were collected from the profiles of 57 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 | 10 | 18% |
| Germany | 7 | 12% |
| United Kingdom | 2 | 4% |
| India | 2 | 4% |
| Australia | 2 | 4% |
| Switzerland | 2 | 4% |
| Korea, Republic of | 1 | 2% |
| China | 1 | 2% |
| Denmark | 1 | 2% |
| Other | 5 | 9% |
| Unknown | 24 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 31 | 54% |
| Scientists | 22 | 39% |
| Science communicators (journalists, bloggers, editors) | 3 | 5% |
| Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 138 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 21% |
| Researcher | 23 | 17% |
| Student > Bachelor | 13 | 9% |
| Student > Master | 12 | 9% |
| Student > Doctoral Student | 8 | 6% |
| Other | 16 | 12% |
| Unknown | 37 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 31 | 22% |
| Agricultural and Biological Sciences | 15 | 11% |
| Computer Science | 15 | 11% |
| Immunology and Microbiology | 9 | 7% |
| Chemistry | 6 | 4% |
| Other | 25 | 18% |
| Unknown | 37 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 32. 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 09 September 2022.
All research outputs
#1,265,558
of 25,712,965 outputs
Outputs from Nature Communications
#19,415
of 58,180 outputs
Outputs of similar age
#32,580
of 460,690 outputs
Outputs of similar age from Nature Communications
#792
of 1,996 outputs
Altmetric has tracked 25,712,965 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 58,180 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.4. This one has gotten more attention than average, scoring higher than 66% 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 460,690 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 92% of its contemporaries.
We're also able to compare this research output to 1,996 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 60% of its contemporaries.