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Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia

Overview of attention for article published in Frontiers in Physiology, June 2023
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1 X user

Readers on

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7 Mendeley
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Title
Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia
Published in
Frontiers in Physiology, June 2023
DOI 10.3389/fphys.2023.1078166
Pubmed ID
Authors

Lilian Bai, Yanyan Guo, Junxing Gong, Yuchen Li, Hefeng Huang, Yicong Meng, Xinmei 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 14%
Student > Postgraduate 1 14%
Unknown 5 71%
Readers by discipline Count As %
Computer Science 2 29%
Unknown 5 71%
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 13 June 2023.
All research outputs
#21,358,731
of 23,854,458 outputs
Outputs from Frontiers in Physiology
#9,937
of 14,580 outputs
Outputs of similar age
#151,197
of 181,967 outputs
Outputs of similar age from Frontiers in Physiology
#95
of 247 outputs
Altmetric has tracked 23,854,458 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 14,580 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. 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 181,967 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 247 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.