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ModularBoost: an efficient network inference algorithm based on module decomposition

Overview of attention for article published in BMC Bioinformatics, March 2021
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
4 X users

Readers on

mendeley
9 Mendeley
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Title
ModularBoost: an efficient network inference algorithm based on module decomposition
Published in
BMC Bioinformatics, March 2021
DOI 10.1186/s12859-021-04074-y
Pubmed ID
Authors

Xinyu Li, Wei Zhang, Jianming Zhang, Guang Li

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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 44%
Professor 1 11%
Student > Doctoral Student 1 11%
Student > Master 1 11%
Unknown 2 22%
Readers by discipline Count As %
Medicine and Dentistry 3 33%
Computer Science 1 11%
Biochemistry, Genetics and Molecular Biology 1 11%
Unknown 4 44%
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 26 March 2021.
All research outputs
#15,145,965
of 23,295,606 outputs
Outputs from BMC Bioinformatics
#5,140
of 7,378 outputs
Outputs of similar age
#246,538
of 429,676 outputs
Outputs of similar age from BMC Bioinformatics
#146
of 165 outputs
Altmetric has tracked 23,295,606 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,378 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 25th percentile – i.e., 25% 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 429,676 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 165 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.