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Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins

Overview of attention for article published in BMC Bioinformatics, February 2013
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Title
Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-68
Pubmed ID
Authors

Jorge Alberto Jaramillo-Garzón, Joan Josep Gallardo-Chacón, César Germán Castellanos-Domínguez, Alexandre Perera-Lluna

Abstract

Proteins are the key elements on the path from genetic information to the development of life. The roles played by the different proteins are difficult to uncover experimentally as this process involves complex procedures such as genetic modifications, injection of fluorescent proteins, gene knock-out methods and others. The knowledge learned from each protein is usually annotated in databases through different methods such as the proposed by The Gene Ontology (GO) consortium. Different methods have been proposed in order to predict GO terms from primary structure information, but very few are available for large-scale functional annotation of plants, and reported success rates are much less than the reported by other non-plant predictors. This paper explores the predictability of GO annotations on proteins belonging to the Embryophyta group from a set of features extracted solely from their primary amino acid sequence.

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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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 20%
Student > Ph. D. Student 5 20%
Researcher 4 16%
Other 3 12%
Student > Master 2 8%
Other 4 16%
Unknown 2 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 28%
Engineering 6 24%
Computer Science 5 20%
Biochemistry, Genetics and Molecular Biology 3 12%
Medicine and Dentistry 1 4%
Other 0 0%
Unknown 3 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 15 March 2013.
All research outputs
#18,333,600
of 22,703,044 outputs
Outputs from BMC Bioinformatics
#6,289
of 7,254 outputs
Outputs of similar age
#146,757
of 192,955 outputs
Outputs of similar age from BMC Bioinformatics
#136
of 159 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.