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Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis

Overview of attention for article published in BMC Bioinformatics, September 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 (55th percentile)

Mentioned by

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6 X users

Citations

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11 Dimensions

Readers on

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24 Mendeley
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Title
Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis
Published in
BMC Bioinformatics, September 2014
DOI 10.1186/1471-2105-15-322
Pubmed ID
Authors

Basel Abu-Jamous, Rui Fa, David J Roberts, Asoke K Nandi

Abstract

The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 4%
Netherlands 1 4%
Unknown 22 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 29%
Researcher 4 17%
Student > Doctoral Student 2 8%
Student > Master 1 4%
Professor 1 4%
Other 2 8%
Unknown 7 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 25%
Computer Science 5 21%
Agricultural and Biological Sciences 3 13%
Medicine and Dentistry 1 4%
Neuroscience 1 4%
Other 1 4%
Unknown 7 29%
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 20 December 2014.
All research outputs
#12,710,028
of 22,764,165 outputs
Outputs from BMC Bioinformatics
#3,621
of 7,273 outputs
Outputs of similar age
#111,929
of 252,543 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 110 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 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 48th percentile – i.e., 48% 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 252,543 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 110 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 55% of its contemporaries.