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Mycofier: a new machine learning-based classifier for fungal ITS sequences

Overview of attention for article published in BMC Research Notes, August 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
30 Mendeley
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Title
Mycofier: a new machine learning-based classifier for fungal ITS sequences
Published in
BMC Research Notes, August 2016
DOI 10.1186/s13104-016-2203-3
Pubmed ID
Authors

Luisa Delgado-Serrano, Silvia Restrepo, Jose Ricardo Bustos, Maria Mercedes Zambrano, Juan Manuel Anzola

Abstract

The taxonomic and phylogenetic classification based on sequence analysis of the ITS1 genomic region has become a crucial component of fungal ecology and diversity studies. Nowadays, there is no accurate alignment-free classification tool for fungal ITS1 sequences for large environmental surveys. This study describes the development of a machine learning-based classifier for the taxonomical assignment of fungal ITS1 sequences at the genus level. A fungal ITS1 sequence database was built using curated data. Training and test sets were generated from it. A Naïve Bayesian classifier was built using features from the primary sequence with an accuracy of 87 % in the classification at the genus level. The final model was based on a Naïve Bayes algorithm using ITS1 sequences from 510 fungal genera. This classifier, denoted as Mycofier, provides similar classification accuracy compared to BLASTN, but the database used for the classification contains curated data and the tool, independent of alignment, is more efficient and contributes to the field, given the lack of an accurate classification tool for large data from fungal ITS1 sequences. The software and source code for Mycofier are freely available at https://github.com/ldelgado-serrano/mycofier.git .

Twitter Demographics

The data shown below were collected from the profiles of 5 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 27%
Student > Ph. D. Student 6 20%
Student > Master 4 13%
Student > Doctoral Student 3 10%
Student > Bachelor 2 7%
Other 4 13%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 53%
Biochemistry, Genetics and Molecular Biology 4 13%
Computer Science 3 10%
Unspecified 1 3%
Immunology and Microbiology 1 3%
Other 2 7%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 06 December 2016.
All research outputs
#5,592,674
of 11,097,556 outputs
Outputs from BMC Research Notes
#795
of 2,453 outputs
Outputs of similar age
#88,382
of 239,152 outputs
Outputs of similar age from BMC Research Notes
#29
of 73 outputs
Altmetric has tracked 11,097,556 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,453 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 67% 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 239,152 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 62% of its contemporaries.
We're also able to compare this research output to 73 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 58% of its contemporaries.