↓ Skip to main content

Representing and querying disease networks using graph databases

Overview of attention for article published in BioData Mining, July 2016
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#15 of 272)
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

twitter
43 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user
f1000
1 research highlight platform

Citations

dimensions_citation
54 Dimensions

Readers on

mendeley
147 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

Twitter Demographics

The data shown below were collected from the profiles of 43 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 147 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 137 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 27%
Student > Ph. D. Student 28 19%
Student > Master 26 18%
Student > Bachelor 10 7%
Other 8 5%
Other 23 16%
Unknown 12 8%
Readers by discipline Count As %
Computer Science 53 36%
Biochemistry, Genetics and Molecular Biology 25 17%
Agricultural and Biological Sciences 19 13%
Medicine and Dentistry 8 5%
Pharmacology, Toxicology and Pharmaceutical Science 3 2%
Other 20 14%
Unknown 19 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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 05 July 2020.
All research outputs
#851,153
of 18,110,549 outputs
Outputs from BioData Mining
#15
of 272 outputs
Outputs of similar age
#18,471
of 272,381 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 18,110,549 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 272 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one has done particularly well, scoring higher than 94% 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 272,381 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 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