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Attention Score in Context
Title |
Automated Alphabet Reduction for Protein Datasets
|
---|---|
Published in |
BMC Bioinformatics, January 2009
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DOI | 10.1186/1471-2105-10-6 |
Pubmed ID | |
Authors |
Jaume Bacardit, Michael Stout, Jonathan D Hirst, Alfonso Valencia, Robert E Smith, Natalio Krasnogor |
Abstract |
We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 20% |
Spain | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 60% |
Members of the public | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 2 | 3% |
United States | 2 | 3% |
India | 1 | 2% |
United Kingdom | 1 | 2% |
Canada | 1 | 2% |
Germany | 1 | 2% |
Russia | 1 | 2% |
Israel | 1 | 2% |
Greece | 1 | 2% |
Other | 1 | 2% |
Unknown | 52 | 81% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 12 | 19% |
Student > Master | 11 | 17% |
Student > Ph. D. Student | 10 | 16% |
Professor > Associate Professor | 6 | 9% |
Professor | 5 | 8% |
Other | 15 | 23% |
Unknown | 5 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 36% |
Computer Science | 13 | 20% |
Biochemistry, Genetics and Molecular Biology | 8 | 13% |
Mathematics | 3 | 5% |
Engineering | 3 | 5% |
Other | 8 | 13% |
Unknown | 6 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 July 2022.
All research outputs
#4,720,129
of 22,888,307 outputs
Outputs from BMC Bioinformatics
#1,817
of 7,298 outputs
Outputs of similar age
#26,460
of 169,998 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 65 outputs
Altmetric has tracked 22,888,307 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 73% 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 169,998 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 78% of its contemporaries.
We're also able to compare this research output to 65 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 69% of its contemporaries.