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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
|
|---|---|
| Published in |
Journal of Cheminformatics, August 2021
|
| DOI | 10.1186/s13321-021-00539-7 |
| Pubmed ID | |
| Authors |
Lewis H. Mervin, Maria-Anna Trapotsi, Avid M. Afzal, Ian P. Barrett, Andreas Bender, Ola Engkvist |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 14% |
| Unknown | 6 | 86% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Scientists | 1 | 14% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 42 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 21% |
| Student > Ph. D. Student | 6 | 14% |
| Student > Master | 4 | 10% |
| Student > Bachelor | 3 | 7% |
| Student > Doctoral Student | 1 | 2% |
| Other | 5 | 12% |
| Unknown | 14 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 6 | 14% |
| Engineering | 4 | 10% |
| Biochemistry, Genetics and Molecular Biology | 3 | 7% |
| Computer Science | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 21 | 50% |
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 03 September 2021.
All research outputs
#7,228,364
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#578
of 891 outputs
Outputs of similar age
#140,089
of 421,258 outputs
Outputs of similar age from Journal of Cheminformatics
#19
of 28 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 35th percentile – i.e., 35% 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 421,258 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 66% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.