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ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins

Overview of attention for article published in BMC Bioinformatics, May 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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5 X users
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1 Wikipedia page

Citations

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59 Dimensions

Readers on

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113 Mendeley
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7 CiteULike
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Title
ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0586-0
Pubmed ID
Authors

Yasser B Ruiz-Blanco, Waldo Paz, James Green, Yovani Marrero-Ponce

Abstract

The exponential growth of protein structural and sequence databases is enabling multifaceted approaches to understanding the long sought sequence-structure-function relationship. Advances in computation now make it possible to apply well-established data mining and pattern recognition techniques to these data to learn models that effectively relate structure and function. However, extracting meaningful numerical descriptors of protein sequence and structure is a key issue that requires an efficient and widely available solution. We here introduce ProtDCal, a new computational software suite capable of generating tens of thousands of features considering both sequence-based and 3D-structural descriptors. We demonstrate, by means of principle component analysis and Shannon entropy tests, how ProtDCal's sequence-based descriptors provide new and more relevant information not encoded by currently available servers for sequence-based protein feature generation. The wide diversity of the 3D-structure-based features generated by ProtDCal is shown to provide additional complementary information and effectively completes its general protein encoding capability. As demonstration of the utility of ProtDCal's features, prediction models of N-linked glycosylation sites are trained and evaluated. Classification performance compares favourably with that of contemporary predictors of N-linked glycosylation sites, in spite of not using domain-specific features as input information. ProtDCal provides a friendly and cross-platform graphical user interface, developed in the Java programming language and is freely available at: http://bioinf.sce.carleton.ca/ProtDCal/ . ProtDCal introduces local and group-based encoding which enhances the diversity of the information captured by the computed features. Furthermore, we have shown that adding structure-based descriptors contributes non-redundant additional information to the features-based characterization of polypeptide systems. This software is intended to provide a useful tool for general-purpose encoding of protein sequences and structures for applications is protein classification, similarity analyses and function prediction.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Canada 1 <1%
Unknown 110 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 19%
Student > Ph. D. Student 14 12%
Student > Master 12 11%
Student > Bachelor 12 11%
Professor 10 9%
Other 26 23%
Unknown 17 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 16%
Chemistry 18 16%
Agricultural and Biological Sciences 16 14%
Computer Science 13 12%
Engineering 8 7%
Other 17 15%
Unknown 23 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 October 2015.
All research outputs
#5,983,850
of 24,143,470 outputs
Outputs from BMC Bioinformatics
#2,068
of 7,506 outputs
Outputs of similar age
#66,548
of 269,214 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 119 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,506 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 71% 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 269,214 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 75% of its contemporaries.
We're also able to compare this research output to 119 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 68% of its contemporaries.