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MiningABs: mining associated biomarkers across multi-connected gene expression datasets

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

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

twitter
4 tweeters
googleplus
1 Google+ user

Readers on

mendeley
40 Mendeley
citeulike
2 CiteULike
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Title
MiningABs: mining associated biomarkers across multi-connected gene expression datasets
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-173
Pubmed ID
Authors

Chun-Pei Cheng, Christopher DeBoever, Kelly A Frazer, Yu-Cheng Liu, Vincent S Tseng

Abstract

Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. Therefore, in this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
Denmark 1 3%
Brazil 1 3%
Unknown 37 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 30%
Student > Ph. D. Student 10 25%
Student > Bachelor 4 10%
Student > Master 3 8%
Other 3 8%
Other 6 15%
Unknown 2 5%
Readers by discipline Count As %
Medicine and Dentistry 11 28%
Biochemistry, Genetics and Molecular Biology 9 23%
Agricultural and Biological Sciences 8 20%
Computer Science 6 15%
Mathematics 1 3%
Other 2 5%
Unknown 3 8%

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 12 June 2014.
All research outputs
#7,394,862
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#2,559
of 5,420 outputs
Outputs of similar age
#68,263
of 190,473 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 14 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 51% 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 190,473 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 63% of its contemporaries.
We're also able to compare this research output to 14 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 64% of its contemporaries.