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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A ranking method for the concurrent learning of compounds with various activity profiles
|
|---|---|
| Published in |
Journal of Cheminformatics, January 2015
|
| DOI | 10.1186/s13321-014-0050-6 |
| Pubmed ID | |
| Authors |
Alexander Dörr, Lars Rosenbaum, Andreas Zell |
| Abstract |
In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 50% |
| Germany | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| Unknown | 30 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 26% |
| Student > Master | 5 | 16% |
| Student > Bachelor | 3 | 10% |
| Student > Postgraduate | 3 | 10% |
| Student > Doctoral Student | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 29% |
| Chemistry | 5 | 16% |
| Engineering | 3 | 10% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Agricultural and Biological Sciences | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 6 | 19% |
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 16 February 2015.
All research outputs
#14,794,387
of 22,778,347 outputs
Outputs from Journal of Cheminformatics
#734
of 829 outputs
Outputs of similar age
#198,101
of 352,360 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 18 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 829 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 10th percentile – i.e., 10% 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 352,360 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% 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 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.