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Identification of distinct miRNA target regulation between breast cancer molecular subtypes using AGO2-PAR-CLIP and patient datasets

Overview of attention for article published in Genome Biology, January 2014
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About this Attention Score

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

Mentioned by

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8 X users

Citations

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64 Dimensions

Readers on

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109 Mendeley
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1 CiteULike
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Title
Identification of distinct miRNA target regulation between breast cancer molecular subtypes using AGO2-PAR-CLIP and patient datasets
Published in
Genome Biology, January 2014
DOI 10.1186/gb-2014-15-1-r9
Pubmed ID
Authors

Thalia A Farazi, Jelle J ten Hoeve, Miguel Brown, Aleksandra Mihailovic, Hugo M Horlings, Marc J van de Vijver, Thomas Tuschl, Lodewyk FA Wessels

Abstract

Various microRNAs (miRNAs) are up- or downregulated in tumors. However, the repression of cognate miRNA targets responsible for the phenotypic effects of this dysregulation in patients remains largely unexplored. To define miRNA targets and associated pathways, together with their relationship to outcome in breast cancer, we integrated patient-paired miRNA-mRNA expression data with a set of validated miRNA targets and pathway inference.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 2%
Germany 1 <1%
France 1 <1%
United Kingdom 1 <1%
Italy 1 <1%
Mexico 1 <1%
United States 1 <1%
Unknown 101 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 27%
Student > Ph. D. Student 26 24%
Student > Master 15 14%
Student > Bachelor 9 8%
Student > Doctoral Student 7 6%
Other 16 15%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 39%
Biochemistry, Genetics and Molecular Biology 27 25%
Medicine and Dentistry 15 14%
Computer Science 8 7%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 6 6%
Unknown 9 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 November 2014.
All research outputs
#7,263,349
of 25,371,288 outputs
Outputs from Genome Biology
#3,289
of 4,467 outputs
Outputs of similar age
#79,239
of 318,504 outputs
Outputs of similar age from Genome Biology
#95
of 115 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 26th percentile – i.e., 26% 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 318,504 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.