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Maximizing gain in high-throughput screening using conformal prediction

Overview of attention for article published in Journal of Cheminformatics, February 2018
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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 (83rd percentile)

Mentioned by

blogs
1 blog
twitter
10 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
28 Mendeley
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Title
Maximizing gain in high-throughput screening using conformal prediction
Published in
Journal of Cheminformatics, February 2018
DOI 10.1186/s13321-018-0260-4
Pubmed ID
Authors

Fredrik Svensson, Avid M. Afzal, Ulf Norinder, Andreas Bender

Abstract

Iterative screening has emerged as a promising approach to increase the efficiency of screening campaigns compared to traditional high throughput approaches. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models, resulting in more efficient screening. One way to evaluate screening is to consider the cost of screening compared to the gain associated with finding an active compound. In this work, we introduce a conformal predictor coupled with a gain-cost function with the aim to maximise gain in iterative screening. Using this setup we were able to show that by evaluating the predictions on the training data, very accurate predictions on what settings will produce the highest gain on the test data can be made. We evaluate the approach on 12 bioactivity datasets from PubChem training the models using 20% of the data. Depending on the settings of the gain-cost function, the settings generating the maximum gain were accurately identified in 8-10 out of the 12 datasets. Broadly, our approach can predict what strategy generates the highest gain based on the results of the cost-gain evaluation: to screen the compounds predicted to be active, to screen all the remaining data, or not to screen any additional compounds. When the algorithm indicates that the predicted active compounds should be screened, our approach also indicates what confidence level to apply in order to maximize gain. Hence, our approach facilitates decision-making and allocation of the resources where they deliver the most value by indicating in advance the likely outcome of a screening campaign.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 46%
Student > Bachelor 5 18%
Student > Master 3 11%
Student > Doctoral Student 2 7%
Other 2 7%
Other 2 7%
Unknown 1 4%
Readers by discipline Count As %
Chemistry 12 43%
Agricultural and Biological Sciences 5 18%
Computer Science 5 18%
Engineering 2 7%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 1 4%
Unknown 2 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 21 August 2019.
All research outputs
#1,453,033
of 14,334,469 outputs
Outputs from Journal of Cheminformatics
#157
of 582 outputs
Outputs of similar age
#45,927
of 274,215 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 14,334,469 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 582 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one has gotten more attention than average, scoring higher than 72% 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 274,215 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 83% of its contemporaries.
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