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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing
|
|---|---|
| Published in |
BMC Bioinformatics, June 2013
|
| DOI | 10.1186/1471-2105-14-181 |
| Pubmed ID | |
| Authors |
Rong Xu, QuanQiu Wang |
| Abstract |
A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Germany | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Netherlands | 1 | <1% |
| Australia | 1 | <1% |
| Hong Kong | 1 | <1% |
| Spain | 1 | <1% |
| Denmark | 1 | <1% |
| Unknown | 106 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 20% |
| Researcher | 22 | 19% |
| Student > Bachelor | 10 | 9% |
| Student > Master | 10 | 9% |
| Student > Doctoral Student | 9 | 8% |
| Other | 23 | 20% |
| Unknown | 17 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 36 | 32% |
| Agricultural and Biological Sciences | 15 | 13% |
| Medicine and Dentistry | 13 | 11% |
| Chemistry | 8 | 7% |
| Biochemistry, Genetics and Molecular Biology | 5 | 4% |
| Other | 16 | 14% |
| Unknown | 21 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 10 July 2013.
All research outputs
#15,272,977
of 22,711,645 outputs
Outputs from BMC Bioinformatics
#5,364
of 7,259 outputs
Outputs of similar age
#122,225
of 197,654 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 109 outputs
Altmetric has tracked 22,711,645 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 197,654 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.