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Multiple-kernel learning for genomic data mining and prediction

Overview of attention for article published in BMC Bioinformatics, August 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

blogs
1 blog
twitter
13 tweeters
Title
Multiple-kernel learning for genomic data mining and prediction
Published in
BMC Bioinformatics, August 2019
DOI 10.1186/s12859-019-2992-1
Pubmed ID
Authors

Christopher M. Wilson, Kaiqiao Li, Xiaoqing Yu, Pei-Fen Kuan, Xuefeng Wang

Twitter Demographics

The data shown below were collected from the profiles of 13 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 11 September 2019.
All research outputs
#1,124,792
of 13,514,416 outputs
Outputs from BMC Bioinformatics
#386
of 5,054 outputs
Outputs of similar age
#22,443
of 166,848 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 13,514,416 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,054 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 92% of its peers.
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 166,848 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them