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A ranking method for the concurrent learning of compounds with various activity profiles

Overview of attention for article published in Journal of Cheminformatics, January 2015
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Mentioned by

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2 tweeters
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1 Facebook page

Citations

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10 Dimensions

Readers on

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28 Mendeley
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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.

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.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 27 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 29%
Student > Master 5 18%
Student > Bachelor 3 11%
Student > Postgraduate 3 11%
Researcher 2 7%
Other 4 14%
Unknown 3 11%
Readers by discipline Count As %
Computer Science 9 32%
Chemistry 5 18%
Biochemistry, Genetics and Molecular Biology 2 7%
Engineering 2 7%
Agricultural and Biological Sciences 2 7%
Other 3 11%
Unknown 5 18%

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
#8,059,698
of 13,388,357 outputs
Outputs from Journal of Cheminformatics
#451
of 541 outputs
Outputs of similar age
#131,061
of 278,483 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 13,388,357 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 541 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one is in the 14th percentile – i.e., 14% 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 278,483 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them