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miRTrail - a comprehensive webserver for analyzing gene and miRNA patterns to enhance the understanding of regulatory mechanisms in diseases

Overview of attention for article published in BMC Bioinformatics, February 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
97 Mendeley
citeulike
10 CiteULike
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Title
miRTrail - a comprehensive webserver for analyzing gene and miRNA patterns to enhance the understanding of regulatory mechanisms in diseases
Published in
BMC Bioinformatics, February 2012
DOI 10.1186/1471-2105-13-36
Pubmed ID
Authors

Cedric Laczny, Petra Leidinger, Jan Haas, Nicole Ludwig, Christina Backes, Andreas Gerasch, Michael Kaufmann, Britta Vogel, Hugo A Katus, Benjamin Meder, Cord Stähler, Eckart Meese, Hans-Peter Lenhof, Andreas Keller

Abstract

Expression profiling provides new insights into regulatory and metabolic processes and in particular into pathogenic mechanisms associated with diseases. Besides genes, non-coding transcripts as microRNAs (miRNAs) gained increasing relevance in the last decade. To understand the regulatory processes of miRNAs on genes, integrative computer-aided approaches are essential, especially in the light of complex human diseases as cancer.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Spain 1 1%
United States 1 1%
Netherlands 1 1%
Unknown 93 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 30%
Student > Ph. D. Student 18 19%
Student > Master 13 13%
Professor 7 7%
Professor > Associate Professor 6 6%
Other 18 19%
Unknown 6 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 40%
Biochemistry, Genetics and Molecular Biology 16 16%
Computer Science 14 14%
Medicine and Dentistry 10 10%
Engineering 3 3%
Other 7 7%
Unknown 8 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 29 February 2012.
All research outputs
#3,313,387
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#1,518
of 4,576 outputs
Outputs of similar age
#29,488
of 116,798 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 24 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 4,576 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 66% 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 116,798 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 74% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.