| Title |
Mining mouse behavior for patterns predicting psychiatric drug classification
|
|---|---|
| Published in |
Psychopharmacology, August 2013
|
| DOI | 10.1007/s00213-013-3230-6 |
| Pubmed ID | |
| Authors |
Neri Kafkafi, Cheryl L. Mayo, Greg I. Elmer |
| Abstract |
In psychiatric drug discovery, a critical step is predicting the psychopharmacological effect and therapeutic potential of novel (or repurposed) compounds early in the development process. This process is hampered by the need to utilize multiple disorder-specific and labor-intensive behavioral assays. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| South Africa | 1 | 2% |
| Unknown | 45 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 12 | 26% |
| Researcher | 9 | 19% |
| Student > Bachelor | 6 | 13% |
| Student > Ph. D. Student | 6 | 13% |
| Student > Doctoral Student | 3 | 6% |
| Other | 7 | 15% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 16 | 34% |
| Neuroscience | 6 | 13% |
| Agricultural and Biological Sciences | 5 | 11% |
| Medicine and Dentistry | 4 | 9% |
| Computer Science | 3 | 6% |
| Other | 7 | 15% |
| Unknown | 6 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 53. 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 28 October 2013.
All research outputs
#672,459
of 22,727,570 outputs
Outputs from Psychopharmacology
#171
of 5,337 outputs
Outputs of similar age
#6,091
of 198,623 outputs
Outputs of similar age from Psychopharmacology
#2
of 50 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,337 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has done particularly well, scoring higher than 96% 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 198,623 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.