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Topology of molecular interaction networks

Overview of attention for article published in BMC Systems Biology, September 2013
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91 Dimensions

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

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

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 216 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 3 1%
Portugal 2 <1%
Netherlands 2 <1%
United Kingdom 2 <1%
Germany 2 <1%
Canada 1 <1%
Iran, Islamic Republic of 1 <1%
Singapore 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 200 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 65 30%
Researcher 46 21%
Student > Master 26 12%
Student > Bachelor 13 6%
Student > Doctoral Student 12 6%
Other 30 14%
Unknown 24 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 70 32%
Biochemistry, Genetics and Molecular Biology 36 17%
Computer Science 30 14%
Engineering 10 5%
Mathematics 7 3%
Other 30 14%
Unknown 33 15%
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 24 March 2023.
All research outputs
#16,844,607
of 25,550,333 outputs
Outputs from BMC Systems Biology
#613
of 1,132 outputs
Outputs of similar age
#120,647
of 199,418 outputs
Outputs of similar age from BMC Systems Biology
#16
of 35 outputs
Altmetric has tracked 25,550,333 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 42nd percentile – i.e., 42% 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 199,418 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.