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Mendeley readers
Attention Score in Context
| Title |
Inferring multi-target QSAR models with taxonomy-based multi-task learning
|
|---|---|
| Published in |
Journal of Cheminformatics, July 2013
|
| DOI | 10.1186/1758-2946-5-33 |
| Pubmed ID | |
| Authors |
Lars Rosenbaum, Alexander Dörr, Matthias R Bauer, Frank M Boeckler, Andreas Zell |
| Abstract |
A plethora of studies indicate that the development of multi-target drugs is beneficial for complex diseases like cancer. Accurate QSAR models for each of the desired targets assist the optimization of a lead candidate by the prediction of affinity profiles. Often, the targets of a multi-target drug are sufficiently similar such that, in principle, knowledge can be transferred between the QSAR models to improve the model accuracy. In this study, we present two different multi-task algorithms from the field of transfer learning that can exploit the similarity between several targets to transfer knowledge between the target specific QSAR models. |
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 % |
|---|---|---|
| Germany | 1 | 50% |
| Unknown | 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 2% |
| United States | 2 | 2% |
| Brazil | 2 | 2% |
| Netherlands | 1 | 1% |
| Japan | 1 | 1% |
| Serbia | 1 | 1% |
| Unknown | 78 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 17% |
| Student > Master | 14 | 16% |
| Student > Bachelor | 9 | 10% |
| Researcher | 8 | 9% |
| Other | 6 | 7% |
| Other | 15 | 17% |
| Unknown | 20 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 20 | 23% |
| Chemistry | 14 | 16% |
| Engineering | 6 | 7% |
| Agricultural and Biological Sciences | 5 | 6% |
| Biochemistry, Genetics and Molecular Biology | 5 | 6% |
| Other | 12 | 14% |
| Unknown | 25 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 15 October 2020.
All research outputs
#4,056,846
of 24,226,848 outputs
Outputs from Journal of Cheminformatics
#378
of 891 outputs
Outputs of similar age
#33,150
of 198,439 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 11 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 10.5. This one has gotten more attention than average, scoring higher than 57% 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 198,439 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.