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Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach

Overview of attention for article published in BMC Oral Health, July 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 793)
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

blogs
2 blogs
twitter
7 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach
Published in
BMC Oral Health, July 2018
DOI 10.1186/s12903-018-0591-6
Pubmed ID
Authors

Yoshio Nakano, Nao Suzuki, Fumiyuki Kuwata

Abstract

Oral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combinations. This paper presents an effective way of deep learning approach to predicting the oral malodour from salivary microbiota. The 16S rRNA genes from saliva samples of 90 subjects (45 had no or weak oral malodour, and 45 had marked oral malodour) were amplified, and gene sequence analysis was carried out. Deep learning classified oral malodour and healthy breath based on the resultant abundances of operational taxonomic units (OTUs) RESULTS: A discrimination classifier model was constructed by profiling OTUs and calculating their relative abundance in saliva samples from 90 subjects. Our deep learning model achieved a predictive accuracy of 97%, compared to the 79% obtained with a support vector machine. This approach is expected to be useful in screening the saliva for prediction of oral malodour before visits to specialist clinics.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 35%
Student > Ph. D. Student 5 19%
Student > Bachelor 2 8%
Other 2 8%
Professor 2 8%
Other 1 4%
Unknown 5 19%
Readers by discipline Count As %
Medicine and Dentistry 7 27%
Biochemistry, Genetics and Molecular Biology 5 19%
Agricultural and Biological Sciences 3 12%
Computer Science 2 8%
Engineering 2 8%
Other 0 0%
Unknown 7 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 27 March 2019.
All research outputs
#1,044,346
of 14,556,858 outputs
Outputs from BMC Oral Health
#27
of 793 outputs
Outputs of similar age
#33,670
of 275,069 outputs
Outputs of similar age from BMC Oral Health
#1
of 1 outputs
Altmetric has tracked 14,556,858 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 793 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 96% 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 275,069 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them