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Twitter Demographics
Mendeley readers
Attention Score in Context
| Title |
The influence of negative training set size on machine learning-based virtual screening
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2014
|
| DOI | 10.1186/1758-2946-6-32 |
| Pubmed ID | |
| Authors |
Rafał Kurczab, Sabina Smusz, Andrzej J Bojarski |
| Abstract |
The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. |
Twitter Demographics
The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| Denmark | 1 | 1% |
| Germany | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 84 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 24% |
| Researcher | 17 | 19% |
| Student > Master | 13 | 15% |
| Student > Bachelor | 4 | 5% |
| Student > Postgraduate | 4 | 5% |
| Other | 11 | 13% |
| Unknown | 18 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 20% |
| Agricultural and Biological Sciences | 14 | 16% |
| Chemistry | 14 | 16% |
| Biochemistry, Genetics and Molecular Biology | 7 | 8% |
| Business, Management and Accounting | 2 | 2% |
| Other | 13 | 15% |
| Unknown | 20 | 23% |
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 03 July 2015.
All research outputs
#15,708,425
of 23,344,526 outputs
Outputs from Journal of Cheminformatics
#780
of 862 outputs
Outputs of similar age
#135,188
of 230,096 outputs
Outputs of similar age from Journal of Cheminformatics
#16
of 18 outputs
Altmetric has tracked 23,344,526 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 862 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 5th percentile – i.e., 5% 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 230,096 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.