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Using Pareto points for model identification in predictive toxicology

Overview of attention for article published in Journal of Cheminformatics, March 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
20 Mendeley
citeulike
2 CiteULike
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Title
Using Pareto points for model identification in predictive toxicology
Published in
Journal of Cheminformatics, March 2013
DOI 10.1186/1758-2946-5-16
Pubmed ID
Authors

Anna Palczewska, Daniel Neagu, Mick Ridley

Abstract

: Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 10%
Germany 1 5%
Unknown 17 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 30%
Researcher 4 20%
Student > Bachelor 2 10%
Other 1 5%
Student > Doctoral Student 1 5%
Other 2 10%
Unknown 4 20%
Readers by discipline Count As %
Chemistry 4 20%
Engineering 3 15%
Computer Science 3 15%
Pharmacology, Toxicology and Pharmaceutical Science 2 10%
Mathematics 1 5%
Other 2 10%
Unknown 5 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 May 2015.
All research outputs
#3,313,903
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#325
of 891 outputs
Outputs of similar age
#27,361
of 200,542 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 14 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 86th 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 10.7. This one has gotten more attention than average, scoring higher than 63% 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 200,542 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 86% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.