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An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling $

Overview of attention for article published in SAR and QSAR in Environmental Research, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 143)
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

blogs
1 blog
twitter
12 tweeters
reddit
1 Redditor

Readers on

mendeley
16 Mendeley
citeulike
2 CiteULike
Title
An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling $
Published in
SAR and QSAR in Environmental Research, November 2016
DOI 10.1080/1062936x.2016.1253611
Pubmed ID
Authors

K. Mansouri, C. M. Grulke, A. M. Richard, R. S. Judson, A. J. Williams

Abstract

The increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experimental data. Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure and identity of chemicals using the publicly available PHYSPROP physicochemical properties and environmental fate datasets. The workflow first assembles structure-identity pairs using up to four provided chemical identifiers, including chemical name, CASRNs, SMILES, and MolBlock. Problems detected included errors and mismatches in chemical structure formats, identifiers and various structure validation issues, including hypervalency and stereochemistry descriptions. Subsequently, a machine learning procedure was applied to evaluate the impact of this curation process. The performance of QSAR models built on only the highest-quality subset of the original dataset was compared with the larger curated and corrected dataset. The latter showed statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and is being made publicly available for further usage and integration by the scientific community.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 6%
United States 1 6%
Unknown 14 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 25%
Student > Ph. D. Student 3 19%
Professor > Associate Professor 3 19%
Student > Master 2 13%
Professor 1 6%
Other 3 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 25%
Chemistry 4 25%
Unspecified 3 19%
Medicine and Dentistry 3 19%
Environmental Science 1 6%
Other 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 31 May 2017.
All research outputs
#579,928
of 8,424,638 outputs
Outputs from SAR and QSAR in Environmental Research
#1
of 143 outputs
Outputs of similar age
#30,546
of 283,724 outputs
Outputs of similar age from SAR and QSAR in Environmental Research
#1
of 4 outputs
Altmetric has tracked 8,424,638 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 143 research outputs from this source. They receive a mean Attention Score of 2.0. This one has done particularly well, scoring higher than 99% 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 283,724 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 89% of its contemporaries.
We're also able to compare this research output to 4 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