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Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

Overview of attention for article published in BMC Bioinformatics, November 2008
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Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
13 Mendeley
citeulike
1 CiteULike
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Title
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
Published in
BMC Bioinformatics, November 2008
DOI 10.1186/1471-2105-9-487
Pubmed ID
Authors

Myron Peto, Andrzej Kloczkowski, Vasant Honavar, Robert L Jernigan

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Researcher 3 23%
Student > Bachelor 1 8%
Other 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 4 31%
Readers by discipline Count As %
Medicine and Dentistry 2 15%
Computer Science 2 15%
Biochemistry, Genetics and Molecular Biology 1 8%
Agricultural and Biological Sciences 1 8%
Physics and Astronomy 1 8%
Other 1 8%
Unknown 5 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 06 July 2016.
All research outputs
#7,485,894
of 22,880,230 outputs
Outputs from BMC Bioinformatics
#3,032
of 7,298 outputs
Outputs of similar age
#48,130
of 166,541 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 44 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,298 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 166,541 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.