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Topic modeling for cluster analysis of large biological and medical datasets

Overview of attention for article published in BMC Bioinformatics, October 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

policy
1 policy source

Citations

dimensions_citation
57 Dimensions

Readers on

mendeley
108 Mendeley
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Title
Topic modeling for cluster analysis of large biological and medical datasets
Published in
BMC Bioinformatics, October 2014
DOI 10.1186/1471-2105-15-s11-s11
Pubmed ID
Authors

Weizhong Zhao, Wen Zou, James J Chen

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Denmark 1 <1%
Unknown 106 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 19%
Researcher 15 14%
Student > Master 15 14%
Student > Bachelor 9 8%
Student > Doctoral Student 8 7%
Other 17 16%
Unknown 23 21%
Readers by discipline Count As %
Computer Science 33 31%
Medicine and Dentistry 11 10%
Engineering 6 6%
Agricultural and Biological Sciences 5 5%
Business, Management and Accounting 5 5%
Other 23 21%
Unknown 25 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 24 August 2022.
All research outputs
#7,594,984
of 23,153,849 outputs
Outputs from BMC Bioinformatics
#3,053
of 7,341 outputs
Outputs of similar age
#85,525
of 260,684 outputs
Outputs of similar age from BMC Bioinformatics
#55
of 132 outputs
Altmetric has tracked 23,153,849 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,341 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 260,684 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 55% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.