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Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity

Overview of attention for article published in Journal of Cheminformatics, March 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 tweeter
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
47 Mendeley
citeulike
1 CiteULike
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Title
Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity
Published in
Journal of Cheminformatics, March 2014
DOI 10.1186/1758-2946-6-8
Pubmed ID
Authors

Samuel J Webb, Thierry Hanser, Brendan Howlin, Paul Krause, Jonathan D Vessey

Abstract

A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model's behaviour for the specific query.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Italy 1 2%
United Kingdom 1 2%
Brazil 1 2%
Unknown 42 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 32%
Student > Bachelor 9 19%
Researcher 9 19%
Student > Master 8 17%
Student > Postgraduate 2 4%
Other 3 6%
Unknown 1 2%
Readers by discipline Count As %
Chemistry 13 28%
Computer Science 12 26%
Medicine and Dentistry 5 11%
Agricultural and Biological Sciences 5 11%
Engineering 3 6%
Other 5 11%
Unknown 4 9%

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 06 August 2014.
All research outputs
#6,760,073
of 12,013,724 outputs
Outputs from Journal of Cheminformatics
#349
of 467 outputs
Outputs of similar age
#81,546
of 191,502 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 16 outputs
Altmetric has tracked 12,013,724 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 467 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one is in the 23rd percentile – i.e., 23% 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 191,502 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.