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Interpretable machine learning methods for predictions in systems biology from omics data

Overview of attention for article published in Frontiers in Molecular Biosciences, October 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
52 Mendeley
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Title
Interpretable machine learning methods for predictions in systems biology from omics data
Published in
Frontiers in Molecular Biosciences, October 2022
DOI 10.3389/fmolb.2022.926623
Pubmed ID
Authors

David Sidak, Jana Schwarzerová, Wolfram Weckwerth, Steffen Waldherr

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
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 %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 15%
Researcher 7 13%
Student > Bachelor 6 12%
Student > Master 6 12%
Student > Doctoral Student 2 4%
Other 7 13%
Unknown 16 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 15%
Biochemistry, Genetics and Molecular Biology 8 15%
Unspecified 5 10%
Mathematics 2 4%
Business, Management and Accounting 2 4%
Other 7 13%
Unknown 20 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 December 2022.
All research outputs
#14,736,776
of 23,885,338 outputs
Outputs from Frontiers in Molecular Biosciences
#1,136
of 4,244 outputs
Outputs of similar age
#200,690
of 430,009 outputs
Outputs of similar age from Frontiers in Molecular Biosciences
#91
of 368 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,244 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 71% 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 430,009 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 368 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 73% of its contemporaries.