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Alignment-free supervised classification of metagenomes by recursive SVM

Overview of attention for article published in BMC Genomics, January 2013
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
17 tweeters
googleplus
1 Google+ user

Citations

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24 Dimensions

Readers on

mendeley
102 Mendeley
citeulike
2 CiteULike
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Title
Alignment-free supervised classification of metagenomes by recursive SVM
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-641
Pubmed ID
Authors

Hongfei Cui, Xuegong Zhang

Abstract

Comparison and classification of metagenome samples is one of the major tasks in the study of microbial communities of natural environments or niches on human bodies. Bioinformatics methods play important roles on this task, including 16S rRNA gene analysis and some alignment-based or alignment-free methods on metagenomic data. Alignment-free methods have the advantage of not depending on known genome annotations and therefore have high potential in studying complicated microbiomes. However, the existing alignment-free methods are all based on unsupervised learning strategy (e.g., PCA or hierarchical clustering). These types of methods are powerful in revealing major similarities and grouping relations between microbiome samples, but cannot be applied for discriminating predefined classes of interest which might not be the dominating assortment in the data. Supervised classification is needed in the latter scenario, with the goal of classifying samples into predefined classes and finding the features that can discriminate the classes. The effectiveness of supervised classification with alignment-based features on metagenomic data have been shown in some recent studies. The application of alignment-free supervised classification methods on metagenome data has not been well explored yet.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Turkey 1 <1%
Sweden 1 <1%
Netherlands 1 <1%
Estonia 1 <1%
Canada 1 <1%
Unknown 94 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 25%
Student > Ph. D. Student 21 21%
Student > Master 16 16%
Student > Bachelor 8 8%
Professor > Associate Professor 8 8%
Other 17 17%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 33%
Computer Science 19 19%
Biochemistry, Genetics and Molecular Biology 14 14%
Medicine and Dentistry 10 10%
Engineering 3 3%
Other 9 9%
Unknown 13 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 11 November 2014.
All research outputs
#2,598,195
of 21,224,592 outputs
Outputs from BMC Genomics
#984
of 10,242 outputs
Outputs of similar age
#24,072
of 185,658 outputs
Outputs of similar age from BMC Genomics
#1
of 17 outputs
Altmetric has tracked 21,224,592 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,242 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 90% 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 185,658 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 17 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 99% of its contemporaries.