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fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

Overview of attention for article published in BMC Bioinformatics, June 2019
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  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
37 Mendeley
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Title
fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies
Published in
BMC Bioinformatics, June 2019
DOI 10.1186/s12859-019-2869-3
Pubmed ID
Authors

Jiaxing Lin, Alexander Sibley, Ivo Shterev, Andrew Nixon, Federico Innocenti, Cliburn Chan, Kouros Owzar

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 24%
Student > Ph. D. Student 6 16%
Student > Bachelor 5 14%
Student > Doctoral Student 2 5%
Student > Master 2 5%
Other 3 8%
Unknown 10 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 16%
Agricultural and Biological Sciences 5 14%
Medicine and Dentistry 4 11%
Computer Science 3 8%
Business, Management and Accounting 1 3%
Other 6 16%
Unknown 12 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 15 June 2019.
All research outputs
#14,452,040
of 23,150,406 outputs
Outputs from BMC Bioinformatics
#4,781
of 7,339 outputs
Outputs of similar age
#193,705
of 353,827 outputs
Outputs of similar age from BMC Bioinformatics
#114
of 186 outputs
Altmetric has tracked 23,150,406 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,339 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% 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 353,827 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 186 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.