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Attention Score in Context
Chapter title |
Identification of Antisense RNA Stem-Loops That Inhibit RNA-Protein Interactions Using a Bacterial Reporter System.
|
---|---|
Chapter number | 4 |
Book title |
RNA-RNA Interactions
|
Published in |
Methods in molecular biology, January 2015
|
DOI | 10.1007/978-1-4939-1896-6_4 |
Pubmed ID | |
Book ISBNs |
978-1-4939-1895-9, 978-1-4939-1896-6
|
Authors |
Kazuo Harada |
Abstract |
RNA-protein interactions play important roles in gene regulation, functional RNA-protein complexes such as the ribosome, and in viral replication. Molecules that regulate specific RNA-protein interactions may be used to dissect biological processes, and to establish the validity of targeting an RNA-protein interaction. There are many examples of biological regulation by antisense RNA stem-loops that form loop-loop and loop-linear RNA-RNA interactions. Here, a bacterial reporter system for the identification of RNA stem-loops that inhibit the formation of RNA-protein complexes through RNA-RNA interactions is described. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 10 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 5 | 50% |
Student > Doctoral Student | 1 | 10% |
Student > Master | 1 | 10% |
Researcher | 1 | 10% |
Student > Postgraduate | 1 | 10% |
Other | 0 | 0% |
Unknown | 1 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 5 | 50% |
Computer Science | 2 | 20% |
Agricultural and Biological Sciences | 2 | 20% |
Engineering | 1 | 10% |
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 30 October 2014.
All research outputs
#15,308,698
of 22,768,097 outputs
Outputs from Methods in molecular biology
#5,319
of 13,090 outputs
Outputs of similar age
#208,770
of 352,897 outputs
Outputs of similar age from Methods in molecular biology
#343
of 996 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,090 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 45th percentile – i.e., 45% 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 352,897 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 996 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.