↓ Skip to main content

Machine Learning for detection of viral sequences in human metagenomic datasets

Overview of attention for article published in BMC Bioinformatics, September 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
7 X users
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
118 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Machine Learning for detection of viral sequences in human metagenomic datasets
Published in
BMC Bioinformatics, September 2018
DOI 10.1186/s12859-018-2340-x
Pubmed ID
Authors

Zurab Bzhalava, Ardi Tampuu, Piotr Bała, Raul Vicente, Joakim Dillner

Abstract

Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as "unknown", as conventional methods find no similarity to known sequences. We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the detection of viral sequences in metagenomic sequencing data. We trained Random Forest and Artificial Neural Network using metagenomic sequences taxonomically classified into virus and non-virus classes. The algorithms achieved accuracies well beyond chance level, with area under ROC curve 0.79. Two codons (TCG and CGC) were found to have a particularly strong discriminative capacity. RSCU-based machine learning techniques applied to metagenomic sequencing data can help identify a large number of putative viral sequences and provide an addition to conventional methods for taxonomic classification.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users 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 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 118 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 15%
Researcher 17 14%
Student > Master 15 13%
Student > Bachelor 12 10%
Student > Doctoral Student 5 4%
Other 14 12%
Unknown 37 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 23%
Agricultural and Biological Sciences 14 12%
Computer Science 12 10%
Immunology and Microbiology 7 6%
Medicine and Dentistry 3 3%
Other 12 10%
Unknown 43 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 23 October 2020.
All research outputs
#2,231,603
of 24,080,653 outputs
Outputs from BMC Bioinformatics
#558
of 7,498 outputs
Outputs of similar age
#46,934
of 344,229 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 106 outputs
Altmetric has tracked 24,080,653 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,498 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 done particularly well, scoring higher than 92% 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 344,229 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 86% of its contemporaries.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.