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Using graph theory to analyze biological networks

Overview of attention for article published in BioData Mining, April 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#42 of 318)
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

twitter
14 X users
patent
2 patents
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
534 Dimensions

Readers on

mendeley
1213 Mendeley
citeulike
25 CiteULike
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Title
Using graph theory to analyze biological networks
Published in
BioData Mining, April 2011
DOI 10.1186/1756-0381-4-10
Pubmed ID
Authors

Georgios A Pavlopoulos, Maria Secrier, Charalampos N Moschopoulos, Theodoros G Soldatos, Sophia Kossida, Jan Aerts, Reinhard Schneider, Pantelis G Bagos

Abstract

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 1,213 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 1%
Germany 13 1%
Brazil 10 <1%
India 8 <1%
France 6 <1%
Spain 5 <1%
United Kingdom 4 <1%
Italy 3 <1%
Belgium 3 <1%
Other 19 2%
Unknown 1129 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 299 25%
Researcher 202 17%
Student > Master 191 16%
Student > Bachelor 126 10%
Student > Doctoral Student 58 5%
Other 181 15%
Unknown 156 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 331 27%
Biochemistry, Genetics and Molecular Biology 196 16%
Computer Science 190 16%
Engineering 65 5%
Mathematics 42 3%
Other 184 15%
Unknown 205 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 2022.
All research outputs
#2,352,022
of 24,585,148 outputs
Outputs from BioData Mining
#42
of 318 outputs
Outputs of similar age
#10,281
of 114,452 outputs
Outputs of similar age from BioData Mining
#1
of 3 outputs
Altmetric has tracked 24,585,148 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 318 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.5. This one has done well, scoring higher than 87% 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 114,452 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 3 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