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A systematic survey of centrality measures for protein-protein interaction networks

Overview of attention for article published in BMC Systems Biology, July 2018
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  • Good Attention Score compared to outputs of the same age (66th percentile)

Mentioned by

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7 tweeters

Citations

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90 Dimensions

Readers on

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154 Mendeley
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Title
A systematic survey of centrality measures for protein-protein interaction networks
Published in
BMC Systems Biology, July 2018
DOI 10.1186/s12918-018-0598-2
Pubmed ID
Authors

Minoo Ashtiani, Ali Salehzadeh-Yazdi, Zahra Razaghi-Moghadam, Holger Hennig, Olaf Wolkenhauer, Mehdi Mirzaie, Mohieddin Jafari

Abstract

Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network's topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities. The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 154 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 18%
Researcher 21 14%
Student > Master 21 14%
Student > Bachelor 19 12%
Professor 8 5%
Other 22 14%
Unknown 36 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 32 21%
Agricultural and Biological Sciences 25 16%
Computer Science 18 12%
Environmental Science 6 4%
Medicine and Dentistry 6 4%
Other 26 17%
Unknown 41 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 08 October 2018.
All research outputs
#6,011,477
of 22,078,848 outputs
Outputs from BMC Systems Biology
#217
of 1,143 outputs
Outputs of similar age
#100,603
of 300,958 outputs
Outputs of similar age from BMC Systems Biology
#1
of 1 outputs
Altmetric has tracked 22,078,848 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,143 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 80% 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 300,958 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 66% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them