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A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

Overview of attention for article published in BMC Systems Biology, March 2012
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Mentioned by

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1 tweeter

Citations

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148 Dimensions

Readers on

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108 Mendeley
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5 CiteULike
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Title
A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data
Published in
BMC Systems Biology, March 2012
DOI 10.1186/1752-0509-6-15
Pubmed ID
Authors

Min Li, Min Li, Hanhui Zhang, Jian-xin Wang, Yi Pan

Abstract

Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 2 2%
Italy 1 <1%
Netherlands 1 <1%
Brazil 1 <1%
India 1 <1%
Germany 1 <1%
Hungary 1 <1%
Unknown 98 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 26%
Researcher 23 21%
Student > Bachelor 12 11%
Student > Master 10 9%
Student > Doctoral Student 6 6%
Other 20 19%
Unknown 9 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 38%
Computer Science 22 20%
Biochemistry, Genetics and Molecular Biology 18 17%
Engineering 3 3%
Medicine and Dentistry 3 3%
Other 7 6%
Unknown 14 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 14 March 2012.
All research outputs
#2,740,534
of 3,631,967 outputs
Outputs from BMC Systems Biology
#357
of 595 outputs
Outputs of similar age
#47,341
of 73,768 outputs
Outputs of similar age from BMC Systems Biology
#8
of 11 outputs
Altmetric has tracked 3,631,967 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 595 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 28th percentile – i.e., 28% 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 73,768 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.