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Efficient prediction of human protein-protein interactions at a global scale

Overview of attention for article published in BMC Bioinformatics, December 2014
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2 X users
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1 Facebook page

Citations

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27 Dimensions

Readers on

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66 Mendeley
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2 CiteULike
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Title
Efficient prediction of human protein-protein interactions at a global scale
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0383-1
Pubmed ID
Authors

Andrew Schoenrock, Bahram Samanfar, Sylvain Pitre, Mohsen Hooshyar, Ke Jin, Charles A Phillips, Hui Wang, Sadhna Phanse, Katayoun Omidi, Yuan Gui, Md Alamgir, Alex Wong, Fredrik Barrenäs, Mohan Babu, Mikael Benson, Michael A Langston, James R Green, Frank Dehne, Ashkan Golshani

Abstract

BackgroundOur knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.ResultsOn the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.ConclusionsThe speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Australia 1 2%
Brazil 1 2%
Canada 1 2%
Spain 1 2%
Unknown 61 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 24%
Researcher 14 21%
Student > Master 12 18%
Student > Bachelor 5 8%
Student > Doctoral Student 4 6%
Other 7 11%
Unknown 8 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 32%
Agricultural and Biological Sciences 15 23%
Computer Science 10 15%
Medicine and Dentistry 4 6%
Engineering 3 5%
Other 4 6%
Unknown 9 14%
Attention Score in Context

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 11 December 2014.
All research outputs
#13,925,649
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#4,471
of 7,276 outputs
Outputs of similar age
#186,358
of 361,216 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 135 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% 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 361,216 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.