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CPANNatNIC software for counter-propagation neural network to assist in read-across

Overview of attention for article published in Journal of Cheminformatics, May 2017
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3 X users

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Title
CPANNatNIC software for counter-propagation neural network to assist in read-across
Published in
Journal of Cheminformatics, May 2017
DOI 10.1186/s13321-017-0218-y
Pubmed ID
Authors

Viktor Drgan, Špela Župerl, Marjan Vračko, Claudia Ileana Cappelli, Marjana Novič

Abstract

CPANNatNIC is software for development of counter-propagation artificial neural network models. Besides the interface for training of a new neural network it also provides an interface for visualisation of the results which was developed to aid in interpretation of the results and to use the program as a tool for read-across. The work presents the details of the program's interface. Parts of the interface are presented and how they can be used. The examples provided show how the user can build a new model and view the results of predictions using the interface. Examples are given to show how the software may be used in read-across. CPANNatNIC provides a simple user interface for model development and visualisation. The interface implements options which may simplify read-across procedure. Statistical results show better prediction accuracy of read-across predictions than model predictions where similar compounds could be identified, which indicates the importance of using read-across and usefulness of the program.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 13%
Student > Master 2 13%
Student > Postgraduate 2 13%
Student > Ph. D. Student 2 13%
Researcher 2 13%
Other 2 13%
Unknown 4 25%
Readers by discipline Count As %
Environmental Science 3 19%
Chemistry 2 13%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Unspecified 1 6%
Other 2 13%
Unknown 6 38%
Attention Score in Context

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 01 June 2017.
All research outputs
#15,207,446
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#755
of 891 outputs
Outputs of similar age
#179,513
of 317,403 outputs
Outputs of similar age from Journal of Cheminformatics
#21
of 21 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
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 is in the 11th percentile – i.e., 11% 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 317,403 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 21 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.