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Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices

Overview of attention for article published in BMC Bioinformatics, January 2007
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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 (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

wikipedia
1 Wikipedia page
q&a
1 Q&A thread

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
40 Mendeley
citeulike
5 CiteULike
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1 Connotea
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Title
Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices
Published in
BMC Bioinformatics, January 2007
DOI 10.1186/1471-2105-8-6
Pubmed ID
Authors

Roger A Craig, Li Liao

Abstract

Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-evolutionary information of the interacting partners, e.g., correlations between distance matrices, where each matrix stores the pairwise distances between a protein and its orthologs from a group of reference genomes.

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 5%
Canada 2 5%
India 1 3%
Sweden 1 3%
Spain 1 3%
Greece 1 3%
United States 1 3%
Unknown 31 78%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 28%
Student > Ph. D. Student 9 23%
Student > Master 5 13%
Student > Postgraduate 4 10%
Professor 2 5%
Other 7 18%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 53%
Biochemistry, Genetics and Molecular Biology 8 20%
Computer Science 4 10%
Medicine and Dentistry 2 5%
Earth and Planetary Sciences 1 3%
Other 2 5%
Unknown 2 5%

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 13 April 2017.
All research outputs
#2,691,852
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#1,081
of 4,576 outputs
Outputs of similar age
#41,616
of 222,631 outputs
Outputs of similar age from BMC Bioinformatics
#56
of 194 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 75% 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 222,631 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 81% of its contemporaries.
We're also able to compare this research output to 194 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 71% of its contemporaries.