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Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators

Overview of attention for article published in BMC Bioinformatics, June 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
50 Mendeley
citeulike
4 CiteULike
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Title
Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-213
Pubmed ID
Authors

Yoichi Murakami, Kenji Mizuguchi

Abstract

Identification of protein-protein interactions (PPIs) is essential for a better understanding of biological processes, pathways and functions. However, experimental identification of the complete set of PPIs in a cell/organism ("an interactome") is still a difficult task. To circumvent limitations of current high-throughput experimental techniques, it is necessary to develop high-performance computational methods for predicting PPIs.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Japan 1 2%
United States 1 2%
Canada 1 2%
Unknown 46 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 34%
Student > Master 8 16%
Researcher 7 14%
Student > Bachelor 7 14%
Student > Doctoral Student 3 6%
Other 2 4%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 38%
Biochemistry, Genetics and Molecular Biology 10 20%
Computer Science 8 16%
Medicine and Dentistry 3 6%
Sports and Recreations 2 4%
Other 2 4%
Unknown 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 July 2014.
All research outputs
#8,208,825
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#3,128
of 5,420 outputs
Outputs of similar age
#80,057
of 188,320 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 3 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 39th percentile – i.e., 39% 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 188,320 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.