↓ Skip to main content

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

Overview of attention for article published in BioData Mining, December 2021
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 tweeters

Readers on

mendeley
17 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus
Published in
BioData Mining, December 2021
DOI 10.1186/s13040-021-00284-5
Pubmed ID
Authors

Chih-Wei Chung, Tzu-Hung Hsiao, Chih-Jen Huang, Yen-Ju Chen, Hsin-Hua Chen, Ching-Heng Lin, Seng-Cho Chou, Tzer-Shyong Chen, Yu-Fang Chung, Hwai-I Yang, Yi-Ming Chen

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 24%
Student > Ph. D. Student 4 24%
Researcher 2 12%
Student > Bachelor 1 6%
Professor 1 6%
Other 1 6%
Unknown 4 24%
Readers by discipline Count As %
Medicine and Dentistry 2 12%
Biochemistry, Genetics and Molecular Biology 2 12%
Agricultural and Biological Sciences 2 12%
Mathematics 1 6%
Business, Management and Accounting 1 6%
Other 4 24%
Unknown 5 29%

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 December 2021.
All research outputs
#14,312,548
of 21,321,610 outputs
Outputs from BioData Mining
#219
of 298 outputs
Outputs of similar age
#268,229
of 462,709 outputs
Outputs of similar age from BioData Mining
#22
of 34 outputs
Altmetric has tracked 21,321,610 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 298 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one is in the 21st percentile – i.e., 21% 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 462,709 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.