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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Integrative relational machine-learning for understanding drug side-effect profiles
|
|---|---|
| Published in |
BMC Bioinformatics, June 2013
|
| DOI | 10.1186/1471-2105-14-207 |
| Pubmed ID | |
| Authors |
Emmanuel Bresso, Renaud Grisoni, Gino Marchetti, Arnaud Sinan Karaboga, Michel Souchet, Marie-Dominique Devignes, Malika Smaïl-Tabbone |
| Abstract |
Drug side effects represent a common reason for stopping drug development during clinical trials.Improving our ability to understand drug side effects is necessary to reduce attrition rates duringdrug development as well as the risk of discovering novel side effects in available drugs.Today, most investigations deal with isolated side effects and overlookpossible redundancy and their frequent co-occurrence. |
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 % |
|---|---|---|
| Canada | 1 | 20% |
| United States | 1 | 20% |
| Spain | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 40% |
| Scientists | 2 | 40% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Netherlands | 1 | 1% |
| Brazil | 1 | 1% |
| China | 1 | 1% |
| India | 1 | 1% |
| Japan | 1 | 1% |
| Spain | 1 | 1% |
| Unknown | 83 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 22 | 24% |
| Student > Ph. D. Student | 14 | 15% |
| Student > Master | 13 | 14% |
| Student > Bachelor | 8 | 9% |
| Other | 5 | 5% |
| Other | 13 | 14% |
| Unknown | 16 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 20 | 22% |
| Agricultural and Biological Sciences | 14 | 15% |
| Medicine and Dentistry | 12 | 13% |
| Biochemistry, Genetics and Molecular Biology | 6 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 3% |
| Other | 13 | 14% |
| Unknown | 23 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 08 September 2016.
All research outputs
#7,552,943
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#2,912
of 7,454 outputs
Outputs of similar age
#63,787
of 198,932 outputs
Outputs of similar age from BMC Bioinformatics
#37
of 85 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,454 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 gotten more attention than average, scoring higher than 60% 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 198,932 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 85 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.