| Title |
Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network
|
|---|---|
| Published in |
Journal of Biomedical Semantics, August 2014
|
| DOI | 10.1186/2041-1480-5-33 |
| Pubmed ID | |
| Authors |
Yuji Zhang, Cui Tao, Guoqian Jiang, Asha A Nair, Jian Su, Christopher G Chute, Hongfang Liu |
| Abstract |
A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 1 | 20% |
| France | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Members of the public | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 4% |
| Japan | 1 | 2% |
| Netherlands | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 46 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 24% |
| Student > Ph. D. Student | 12 | 24% |
| Researcher | 8 | 16% |
| Student > Doctoral Student | 4 | 8% |
| Professor | 3 | 6% |
| Other | 3 | 6% |
| Unknown | 9 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 27% |
| Biochemistry, Genetics and Molecular Biology | 8 | 16% |
| Agricultural and Biological Sciences | 6 | 12% |
| Medicine and Dentistry | 6 | 12% |
| Nursing and Health Professions | 1 | 2% |
| Other | 5 | 10% |
| Unknown | 11 | 22% |
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 16 March 2022.
All research outputs
#3,211,933
of 25,371,288 outputs
Outputs from Journal of Biomedical Semantics
#41
of 368 outputs
Outputs of similar age
#31,254
of 241,614 outputs
Outputs of similar age from Journal of Biomedical Semantics
#2
of 13 outputs
Altmetric has tracked 25,371,288 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 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 88% 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 241,614 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 13 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.