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Topology of molecular interaction networks

Overview of attention for article published in BMC Systems Biology, January 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

2 tweeters
1 Wikipedia page


69 Dimensions

Readers on

202 Mendeley
5 CiteULike
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Topology of molecular interaction networks
Published in
BMC Systems Biology, January 2013
DOI 10.1186/1752-0509-7-90
Pubmed ID

Wynand Winterbach, Piet Van Mieghem, Marcel Reinders, Huijuan Wang, Dick de Ridder


Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 202 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 3 1%
Netherlands 2 <1%
Germany 2 <1%
United Kingdom 2 <1%
Portugal 2 <1%
Canada 1 <1%
Iran, Islamic Republic of 1 <1%
Singapore 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 186 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 64 32%
Researcher 44 22%
Student > Master 26 13%
Student > Doctoral Student 11 5%
Student > Bachelor 11 5%
Other 30 15%
Unknown 16 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 70 35%
Biochemistry, Genetics and Molecular Biology 36 18%
Computer Science 28 14%
Engineering 11 5%
Mathematics 7 3%
Other 27 13%
Unknown 23 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 May 2021.
All research outputs
of 18,652,463 outputs
Outputs from BMC Systems Biology
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Outputs of similar age
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Outputs of similar age from BMC Systems Biology
of 18 outputs
Altmetric has tracked 18,652,463 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,127 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 77% 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 178,841 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 71% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.