Title |
Representing and querying disease networks using graph databases
|
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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
Geographical breakdown
Country | Count | As % |
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United States | 5 | 13% |
France | 4 | 10% |
United Kingdom | 4 | 10% |
Mexico | 3 | 8% |
Spain | 2 | 5% |
Philippines | 1 | 3% |
Netherlands | 1 | 3% |
Indonesia | 1 | 3% |
Ecuador | 1 | 3% |
Other | 4 | 10% |
Unknown | 13 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 21 | 54% |
Scientists | 18 | 46% |
Mendeley readers
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% |