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Phylogenetic convolutional neural networks in metagenomics

Overview of attention for article published in BMC Bioinformatics, March 2018
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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 (91st percentile)

Mentioned by

blogs
1 blog
twitter
21 tweeters

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
141 Mendeley
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Title
Phylogenetic convolutional neural networks in metagenomics
Published in
BMC Bioinformatics, March 2018
DOI 10.1186/s12859-018-2033-5
Pubmed ID
Authors

Diego Fioravanti, Ylenia Giarratano, Valerio Maggio, Claudio Agostinelli, Marco Chierici, Giuseppe Jurman, Cesare Furlanello

Abstract

Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 141 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 30%
Researcher 24 17%
Student > Bachelor 17 12%
Student > Master 16 11%
Student > Doctoral Student 6 4%
Other 21 15%
Unknown 14 10%
Readers by discipline Count As %
Computer Science 42 30%
Agricultural and Biological Sciences 26 18%
Biochemistry, Genetics and Molecular Biology 25 18%
Engineering 8 6%
Immunology and Microbiology 4 3%
Other 16 11%
Unknown 20 14%

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 21 December 2018.
All research outputs
#1,049,707
of 14,515,191 outputs
Outputs from BMC Bioinformatics
#298
of 5,423 outputs
Outputs of similar age
#35,043
of 276,822 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 24 outputs
Altmetric has tracked 14,515,191 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 5,423 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 94% 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 276,822 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 24 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.