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Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer

Overview of attention for article published in BMC Systems Biology, July 2014
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Mentioned by

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1 tweeter

Citations

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182 Dimensions

Readers on

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132 Mendeley
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1 CiteULike

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Title
Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer
Published in
BMC Systems Biology, July 2014
DOI 10.1186/1752-0509-8-83
Pubmed ID
25033876
Authors

Paola Paci, Teresa Colombo, Lorenzo Farina

Abstract

Non-coding RNAs (ncRNAs) are emerging as key regulators of many cellular processes in both physiological and pathological states. Moreover, the constant discovery of new non-coding RNA species suggests that the study of their complex functions is still in its very early stages. This variegated class of RNA species encompasses the well-known microRNAs (miRNAs) and the most recently acknowledged long non-coding RNAs (lncRNAs). Interestingly, in the last couple of years, a few studies have shown that some lncRNAs can act as miRNA sponges, i.e. as competing endogenous RNAs (ceRNAs), able to reduce the amount of miRNAs available to target messenger RNAs (mRNAs).

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Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Hungary 2 2%
Italy 2 2%
Denmark 1 <1%
Malaysia 1 <1%
Brazil 1 <1%
China 1 <1%
Spain 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 118 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 28%
Researcher 21 16%
Student > Master 14 11%
Student > Bachelor 12 9%
Student > Postgraduate 10 8%
Other 21 16%
Unknown 17 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 32%
Biochemistry, Genetics and Molecular Biology 38 29%
Medicine and Dentistry 11 8%
Computer Science 10 8%
Nursing and Health Professions 3 2%
Other 8 6%
Unknown 20 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 August 2014.
All research outputs
#13,894,219
of 17,414,782 outputs
Outputs from BMC Systems Biology
#803
of 1,115 outputs
Outputs of similar age
#138,524
of 201,218 outputs
Outputs of similar age from BMC Systems Biology
#3
of 4 outputs
Altmetric has tracked 17,414,782 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,115 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 12th percentile – i.e., 12% 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 201,218 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.

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