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TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

Overview of attention for article published in BMC Bioinformatics, October 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

Citations

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7 Dimensions

Readers on

mendeley
7 Mendeley
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Title
TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery
Published in
BMC Bioinformatics, October 2021
DOI 10.1186/s12859-021-03964-5
Pubmed ID
Authors

Yusong Liu, Xiufen Ye, Christina Y. Yu, Wei Shao, Jie Hou, Weixing Feng, Jie Zhang, Kun Huang

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

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 29%
Unknown 5 71%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 14%
Computer Science 1 14%
Unknown 5 71%
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 October 2021.
All research outputs
#15,155,790
of 23,310,485 outputs
Outputs from BMC Bioinformatics
#5,151
of 7,382 outputs
Outputs of similar age
#232,741
of 439,102 outputs
Outputs of similar age from BMC Bioinformatics
#137
of 165 outputs
Altmetric has tracked 23,310,485 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 7,382 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 439,102 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% 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 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.