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Predicting RNA-Protein Interactions Using Only Sequence Information

Overview of attention for article published in BMC Bioinformatics, December 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
7 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
192 Dimensions

Readers on

mendeley
220 Mendeley
citeulike
6 CiteULike
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Title
Predicting RNA-Protein Interactions Using Only Sequence Information
Published in
BMC Bioinformatics, December 2011
DOI 10.1186/1471-2105-12-489
Pubmed ID
Authors

Usha K Muppirala, Vasant G Honavar, Drena Dobbs

Abstract

RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 220 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Germany 2 <1%
India 2 <1%
Canada 1 <1%
Brazil 1 <1%
Mexico 1 <1%
China 1 <1%
Japan 1 <1%
France 1 <1%
Other 1 <1%
Unknown 205 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 68 31%
Student > Master 36 16%
Researcher 36 16%
Student > Bachelor 16 7%
Student > Postgraduate 12 5%
Other 27 12%
Unknown 25 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 83 38%
Biochemistry, Genetics and Molecular Biology 40 18%
Computer Science 34 15%
Medicine and Dentistry 10 5%
Chemistry 6 3%
Other 16 7%
Unknown 31 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 18 January 2020.
All research outputs
#2,837,976
of 15,547,899 outputs
Outputs from BMC Bioinformatics
#1,192
of 5,671 outputs
Outputs of similar age
#35,142
of 219,499 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 344 outputs
Altmetric has tracked 15,547,899 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,671 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 78% 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 219,499 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 83% of its contemporaries.
We're also able to compare this research output to 344 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.