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Interpreting linear support vector machine models with heat map molecule coloring

Overview of attention for article published in Journal of Cheminformatics, March 2011
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1 Google+ user

Citations

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44 Dimensions

Readers on

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75 Mendeley
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2 CiteULike
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Title
Interpreting linear support vector machine models with heat map molecule coloring
Published in
Journal of Cheminformatics, March 2011
DOI 10.1186/1758-2946-3-11
Pubmed ID
Authors

Lars Rosenbaum, Georg Hinselmann, Andreas Jahn, Andreas Zell

Abstract

Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Germany 2 3%
India 1 1%
Unknown 70 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 20%
Student > Ph. D. Student 13 17%
Student > Master 8 11%
Student > Bachelor 8 11%
Other 7 9%
Other 12 16%
Unknown 12 16%
Readers by discipline Count As %
Chemistry 17 23%
Computer Science 15 20%
Agricultural and Biological Sciences 10 13%
Engineering 7 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 9 12%
Unknown 15 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 March 2012.
All research outputs
#7,801,184
of 12,434,464 outputs
Outputs from Journal of Cheminformatics
#422
of 494 outputs
Outputs of similar age
#63,618
of 116,188 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 6 outputs
Altmetric has tracked 12,434,464 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 494 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 9th percentile – i.e., 9% 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 116,188 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.