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Identification of Potential Diagnostic Gene Targets for Pediatric Sepsis Based on Bioinformatics and Machine Learning

Overview of attention for article published in Frontiers in Pediatrics, March 2021
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Mentioned by

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1 X user

Citations

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3 Dimensions

Readers on

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24 Mendeley
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Title
Identification of Potential Diagnostic Gene Targets for Pediatric Sepsis Based on Bioinformatics and Machine Learning
Published in
Frontiers in Pediatrics, March 2021
DOI 10.3389/fped.2021.576585
Pubmed ID
Authors

Ying Qiao, Bo Zhang, Ying Liu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 6 25%
Student > Ph. D. Student 3 13%
Student > Master 3 13%
Student > Doctoral Student 1 4%
Other 1 4%
Other 1 4%
Unknown 9 38%
Readers by discipline Count As %
Unspecified 6 25%
Medicine and Dentistry 5 21%
Computer Science 1 4%
Economics, Econometrics and Finance 1 4%
Engineering 1 4%
Other 0 0%
Unknown 10 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 10 March 2021.
All research outputs
#20,690,174
of 23,287,285 outputs
Outputs from Frontiers in Pediatrics
#4,311
of 6,271 outputs
Outputs of similar age
#361,500
of 420,308 outputs
Outputs of similar age from Frontiers in Pediatrics
#212
of 335 outputs
Altmetric has tracked 23,287,285 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,271 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 420,308 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 335 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.