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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Exploiting large-scale drug-protein interaction information for computational drug repurposing
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-210 |
| Pubmed ID | |
| Authors |
Ruifeng Liu, Narender Singh, Gregory J Tawa, Anders Wallqvist, Jaques Reifman |
| Abstract |
Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 25% |
| Spain | 1 | 25% |
| Norway | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| United Kingdom | 1 | 1% |
| Netherlands | 1 | 1% |
| Unknown | 79 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 22% |
| Student > Ph. D. Student | 17 | 20% |
| Student > Master | 11 | 13% |
| Student > Bachelor | 7 | 8% |
| Student > Doctoral Student | 3 | 4% |
| Other | 11 | 13% |
| Unknown | 16 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 14% |
| Biochemistry, Genetics and Molecular Biology | 10 | 12% |
| Chemistry | 9 | 11% |
| Medicine and Dentistry | 9 | 11% |
| Computer Science | 8 | 10% |
| Other | 12 | 14% |
| Unknown | 23 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 24 June 2014.
All research outputs
#13,714,534
of 22,757,541 outputs
Outputs from BMC Bioinformatics
#4,440
of 7,272 outputs
Outputs of similar age
#114,984
of 228,326 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 154 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 38th percentile – i.e., 38% 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 228,326 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.