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MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease

Overview of attention for article published in BMC Genomic Data, August 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
14 Mendeley
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Title
MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease
Published in
BMC Genomic Data, August 2020
DOI 10.1186/s12863-020-00899-3
Pubmed ID
Authors

Yuchen Zhu, Jiadong Ji, Weiqiang Lin, Mingzhuo Li, Lu Liu, Huanhuan Zhu, Fuzhong Xue, Xiujun Li, Xiang Zhou, Zhongshang Yuan

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Doctoral Student 2 14%
Student > Bachelor 1 7%
Student > Ph. D. Student 1 7%
Student > Postgraduate 1 7%
Other 0 0%
Unknown 6 43%
Readers by discipline Count As %
Medicine and Dentistry 4 29%
Computer Science 2 14%
Agricultural and Biological Sciences 1 7%
Unknown 7 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 September 2020.
All research outputs
#4,839,541
of 25,387,668 outputs
Outputs from BMC Genomic Data
#164
of 1,204 outputs
Outputs of similar age
#113,954
of 424,952 outputs
Outputs of similar age from BMC Genomic Data
#4
of 22 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 85% 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 424,952 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.