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Gaussian process regression model for normalization of LC-MS data using scan-level information

Overview of attention for article published in Proteome Science, November 2013
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About this Attention Score

  • Among the highest-scoring outputs from this source (#43 of 205)
  • Average Attention Score compared to outputs of the same age

Mentioned by

patent
1 patent

Readers on

mendeley
52 Mendeley
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Title
Gaussian process regression model for normalization of LC-MS data using scan-level information
Published in
Proteome Science, November 2013
DOI 10.1186/1477-5956-11-s1-s13
Pubmed ID
Authors

Mohammad R Nezami Ranjbar, Yi Zhao, Mahlet G Tadesse, Yue Wang, Habtom W Ressom

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
South Africa 2 4%
Spain 1 2%
Unknown 49 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 17%
Student > Master 7 13%
Researcher 6 12%
Professor > Associate Professor 3 6%
Student > Bachelor 2 4%
Other 5 10%
Unknown 20 38%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 29%
Chemistry 6 12%
Biochemistry, Genetics and Molecular Biology 3 6%
Medicine and Dentistry 3 6%
Mathematics 1 2%
Other 5 10%
Unknown 19 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 14 December 2023.
All research outputs
#8,398,581
of 25,097,836 outputs
Outputs from Proteome Science
#43
of 205 outputs
Outputs of similar age
#75,146
of 223,035 outputs
Outputs of similar age from Proteome Science
#1
of 1 outputs
Altmetric has tracked 25,097,836 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 205 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 56% 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 223,035 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them