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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting disease associations via biological network analysis
|
|---|---|
| Published in |
BMC Bioinformatics, September 2014
|
| DOI | 10.1186/1471-2105-15-304 |
| Pubmed ID | |
| Authors |
Kai Sun, Joana P Gonçalves, Chris Larminie, Nataša Pržulj |
| Abstract |
Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment. |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 11% |
| United States | 2 | 11% |
| Italy | 1 | 5% |
| Australia | 1 | 5% |
| India | 1 | 5% |
| Colombia | 1 | 5% |
| United Kingdom | 1 | 5% |
| Unknown | 10 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 53% |
| Members of the public | 7 | 37% |
| Practitioners (doctors, other healthcare professionals) | 2 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 2% |
| Spain | 2 | 1% |
| Brazil | 1 | <1% |
| India | 1 | <1% |
| France | 1 | <1% |
| Canada | 1 | <1% |
| Netherlands | 1 | <1% |
| Singapore | 1 | <1% |
| United States | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 141 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 43 | 28% |
| Researcher | 33 | 22% |
| Student > Master | 19 | 12% |
| Student > Postgraduate | 10 | 7% |
| Student > Doctoral Student | 6 | 4% |
| Other | 19 | 12% |
| Unknown | 23 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 48 | 31% |
| Agricultural and Biological Sciences | 29 | 19% |
| Biochemistry, Genetics and Molecular Biology | 20 | 13% |
| Medicine and Dentistry | 8 | 5% |
| Neuroscience | 3 | 2% |
| Other | 13 | 8% |
| Unknown | 32 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 26 November 2014.
All research outputs
#3,182,884
of 25,252,667 outputs
Outputs from BMC Bioinformatics
#1,004
of 7,664 outputs
Outputs of similar age
#33,140
of 256,508 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 113 outputs
Altmetric has tracked 25,252,667 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,664 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 86% 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 256,508 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.