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Representing and querying disease networks using graph databases

Overview of attention for article published in BioData Mining, July 2016
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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 (#15 of 324)
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
39 X users
facebook
1 Facebook page
googleplus
1 Google+ user
f1000
1 research highlight platform

Citations

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77 Dimensions

Readers on

mendeley
159 Mendeley
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Title
Representing and querying disease networks using graph databases
Published in
BioData Mining, July 2016
DOI 10.1186/s13040-016-0102-8
Pubmed ID
Authors

Artem Lysenko, Irina A. Roznovăţ, Mansoor Saqi, Alexander Mazein, Christopher J Rawlings, Charles Auffray

Abstract

Systems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data. We show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes. Our study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 39 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 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Turkey 1 <1%
Australia 1 <1%
Brazil 1 <1%
Sweden 1 <1%
Israel 1 <1%
United Kingdom 1 <1%
Korea, Republic of 1 <1%
Spain 1 <1%
United States 1 <1%
Other 1 <1%
Unknown 149 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 26%
Student > Ph. D. Student 32 20%
Student > Master 28 18%
Student > Bachelor 10 6%
Other 8 5%
Other 22 14%
Unknown 18 11%
Readers by discipline Count As %
Computer Science 56 35%
Biochemistry, Genetics and Molecular Biology 26 16%
Agricultural and Biological Sciences 20 13%
Medicine and Dentistry 8 5%
Engineering 4 3%
Other 20 13%
Unknown 25 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 07 March 2024.
All research outputs
#1,337,606
of 25,440,205 outputs
Outputs from BioData Mining
#15
of 324 outputs
Outputs of similar age
#25,155
of 380,013 outputs
Outputs of similar age from BioData Mining
#2
of 7 outputs
Altmetric has tracked 25,440,205 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 324 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has done particularly well, scoring higher than 95% 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 380,013 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 93% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.