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Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets

Overview of attention for article published in BMC Medical Genomics, April 2020
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Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
4 Mendeley
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Title
Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets
Published in
BMC Medical Genomics, April 2020
DOI 10.1186/s12920-020-0684-3
Pubmed ID
Authors

Xiaoyu Cai, Lo-Bin Chang, Jordan Potter, Chi Song

Twitter Demographics

The data shown below were collected from the profiles of 2 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 25%
Student > Ph. D. Student 1 25%
Student > Bachelor 1 25%
Unknown 1 25%
Readers by discipline Count As %
Mathematics 1 25%
Business, Management and Accounting 1 25%
Unknown 2 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 04 April 2020.
All research outputs
#13,855,613
of 17,366,233 outputs
Outputs from BMC Medical Genomics
#673
of 915 outputs
Outputs of similar age
#205,215
of 278,864 outputs
Outputs of similar age from BMC Medical Genomics
#5
of 7 outputs
Altmetric has tracked 17,366,233 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 915 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 14th percentile – i.e., 14% 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 278,864 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.