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Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies

Overview of attention for article published in Frontiers in immunology, April 2023
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1 X user

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8 Mendeley
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Title
Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies
Published in
Frontiers in immunology, April 2023
DOI 10.3389/fimmu.2023.1164408
Pubmed ID
Authors

Qihui Wu, Ruotong Tian, Xiaoyun He, Jiaxin Liu, Chunlin Ou, Yimin Li, Xiaodan Fu

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X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 13%
Student > Bachelor 1 13%
Researcher 1 13%
Other 1 13%
Unknown 4 50%
Readers by discipline Count As %
Arts and Humanities 1 13%
Biochemistry, Genetics and Molecular Biology 1 13%
Agricultural and Biological Sciences 1 13%
Medicine and Dentistry 1 13%
Unknown 4 50%
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 24 April 2023.
All research outputs
#21,321,749
of 26,171,302 outputs
Outputs from Frontiers in immunology
#25,510
of 32,855 outputs
Outputs of similar age
#322,199
of 428,917 outputs
Outputs of similar age from Frontiers in immunology
#1,109
of 1,433 outputs
Altmetric has tracked 26,171,302 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 32,855 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one is in the 13th percentile – i.e., 13% 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 428,917 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,433 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.