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Kernel principal components based cascade forest towards disease identification with human microbiota

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2021
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Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
12 Mendeley
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Title
Kernel principal components based cascade forest towards disease identification with human microbiota
Published in
BMC Medical Informatics and Decision Making, December 2021
DOI 10.1186/s12911-021-01705-5
Pubmed ID
Authors

Jiayu Zhou, Yanqing Ye, Jiang

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 17%
Other 2 17%
Professor 1 8%
Professor > Associate Professor 1 8%
Unspecified 1 8%
Other 0 0%
Unknown 5 42%
Readers by discipline Count As %
Unspecified 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Agricultural and Biological Sciences 1 8%
Computer Science 1 8%
Immunology and Microbiology 1 8%
Other 1 8%
Unknown 6 50%

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 December 2021.
All research outputs
#20,231,392
of 22,757,090 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,798
of 1,985 outputs
Outputs of similar age
#405,484
of 495,935 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#49
of 63 outputs
Altmetric has tracked 22,757,090 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 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. 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 495,935 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 63 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.