↓ Skip to main content

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Overview of attention for article published in Nature Communications, June 2021
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
57 X users

Citations

dimensions_citation
112 Dimensions

Readers on

mendeley
143 Mendeley
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

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 143 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 20%
Researcher 24 17%
Student > Bachelor 14 10%
Student > Master 12 8%
Student > Doctoral Student 8 6%
Other 16 11%
Unknown 40 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 32 22%
Agricultural and Biological Sciences 15 10%
Computer Science 15 10%
Immunology and Microbiology 9 6%
Chemistry 6 4%
Other 26 18%
Unknown 40 28%
Attention Score in Context

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
#1,274,987
of 25,850,376 outputs
Outputs from Nature Communications
#19,577
of 58,721 outputs
Outputs of similar age
#32,696
of 461,364 outputs
Outputs of similar age from Nature Communications
#792
of 1,996 outputs
Altmetric has tracked 25,850,376 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 58,721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.3. 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 461,364 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.