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Mining breast cancer genes with a network based noise-tolerant approach

Overview of attention for article published in BMC Systems Biology, June 2013
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
37 Mendeley
citeulike
1 CiteULike
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Title
Mining breast cancer genes with a network based noise-tolerant approach
Published in
BMC Systems Biology, June 2013
DOI 10.1186/1752-0509-7-49
Pubmed ID
Abstract

Mining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it's important to design methods that work robustly with respect to noise.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
China 1 3%
Germany 1 3%
Unknown 33 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 30%
Student > Ph. D. Student 10 27%
Student > Master 7 19%
Professor > Associate Professor 2 5%
Student > Bachelor 1 3%
Other 6 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 35%
Computer Science 10 27%
Biochemistry, Genetics and Molecular Biology 5 14%
Engineering 2 5%
Nursing and Health Professions 1 3%
Other 5 14%
Unknown 1 3%

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 27 June 2013.
All research outputs
#2,124,364
of 4,507,280 outputs
Outputs from BMC Systems Biology
#272
of 670 outputs
Outputs of similar age
#40,717
of 89,548 outputs
Outputs of similar age from BMC Systems Biology
#7
of 23 outputs
Altmetric has tracked 4,507,280 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 670 research outputs from this source. They receive a mean Attention Score of 2.8. This one has gotten more attention than average, scoring higher than 54% 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 89,548 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 50% of its contemporaries.
We're also able to compare this research output to 23 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 56% of its contemporaries.