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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
1 blog
twitter
6 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
392 Dimensions

Readers on

mendeley
290 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 290 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
India 2 <1%
Germany 2 <1%
Brazil 1 <1%
France 1 <1%
Mexico 1 <1%
Canada 1 <1%
Japan 1 <1%
Poland 1 <1%
Other 0 0%
Unknown 276 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 84 29%
Student > Master 44 15%
Researcher 43 15%
Student > Bachelor 18 6%
Student > Postgraduate 14 5%
Other 37 13%
Unknown 50 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 86 30%
Biochemistry, Genetics and Molecular Biology 63 22%
Computer Science 38 13%
Medicine and Dentistry 16 6%
Chemistry 8 3%
Other 26 9%
Unknown 53 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 15 May 2023.
All research outputs
#2,438,108
of 24,262,436 outputs
Outputs from BMC Bioinformatics
#657
of 7,510 outputs
Outputs of similar age
#18,417
of 250,773 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 100 outputs
Altmetric has tracked 24,262,436 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,510 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 91% 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 250,773 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.