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Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

Overview of attention for article published in Journal of Cheminformatics, September 2013
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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 (87th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users
googleplus
1 Google+ user

Citations

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68 Dimensions

Readers on

mendeley
168 Mendeley
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4 CiteULike
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Title
Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets
Published in
Journal of Cheminformatics, September 2013
DOI 10.1186/1758-2946-5-42
Pubmed ID
Authors

Gerard JP van Westen, Remco F Swier, Isidro Cortes-Ciriano, Jörg K Wegner, John P Overington, Adriaan P IJzerman, Herman WT van Vlijmen, Andreas Bender

Abstract

While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
India 2 1%
Bulgaria 1 <1%
Italy 1 <1%
Iran, Islamic Republic of 1 <1%
Japan 1 <1%
Unknown 160 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 23%
Student > Ph. D. Student 26 15%
Student > Master 23 14%
Student > Bachelor 17 10%
Student > Doctoral Student 9 5%
Other 26 15%
Unknown 28 17%
Readers by discipline Count As %
Chemistry 39 23%
Computer Science 26 15%
Biochemistry, Genetics and Molecular Biology 25 15%
Agricultural and Biological Sciences 19 11%
Pharmacology, Toxicology and Pharmaceutical Science 13 8%
Other 17 10%
Unknown 29 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 14 October 2013.
All research outputs
#2,805,778
of 22,723,682 outputs
Outputs from Journal of Cheminformatics
#287
of 828 outputs
Outputs of similar age
#26,375
of 203,069 outputs
Outputs of similar age from Journal of Cheminformatics
#3
of 7 outputs
Altmetric has tracked 22,723,682 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has gotten more attention than average, scoring higher than 65% 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 203,069 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 87% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.