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ProQ3D: improved model quality assessments using deep learning

Overview of attention for article published in Bioinformatics, January 2017
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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1 X user
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1 patent

Citations

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158 Dimensions

Readers on

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98 Mendeley
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1 CiteULike
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Title
ProQ3D: improved model quality assessments using deep learning
Published in
Bioinformatics, January 2017
DOI 10.1093/bioinformatics/btw819
Pubmed ID
Authors

Karolis Uziela, David Menéndez Hurtado, Nanjiang Shu, Björn Wallner, Arne Elofsson

Abstract

Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features). ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/Contact:[email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 98 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 21%
Student > Bachelor 14 14%
Researcher 10 10%
Student > Master 10 10%
Student > Doctoral Student 7 7%
Other 13 13%
Unknown 23 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 30 31%
Computer Science 12 12%
Agricultural and Biological Sciences 12 12%
Chemistry 6 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 11 11%
Unknown 25 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 March 2020.
All research outputs
#7,041,787
of 23,043,346 outputs
Outputs from Bioinformatics
#1,994
of 4,057 outputs
Outputs of similar age
#131,171
of 421,925 outputs
Outputs of similar age from Bioinformatics
#38
of 90 outputs
Altmetric has tracked 23,043,346 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 4,057 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one is in the 49th percentile – i.e., 49% 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 421,925 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 68% of its contemporaries.
We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.