Phylogeny-Aware Deep 1-Dimensional Convolutional Neural Network for the Classification of Metagenomes

Timmy Manning; Jyotsna Talreja Wassan; Cintia Palu; Haiying / HY Wang; Browne Fiona; Huiru Zheng; Brian Kelly; Paul Walsh

doi:10.1109/BIBM.2018.8621543

Phylogeny-Aware Deep 1-Dimensional Convolutional Neural Network for the Classification of Metagenomes

Timmy Manning
, Jyotsna Talreja Wassan
, Cintia Palu
, Haiying / HY Wang
, Browne Fiona
, Huiru Zheng
, Brian Kelly
, Paul Walsh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

This paper evaluates a novel approach to the integration of biological domain knowledge relating to the natural evolutionary structure of microbial community data to classifying 16S rDNA sequence samples. Specifically, we evaluate the use of phylogenetic trees in addition to amplicon sequence variant abundance in samples for the classification of a processed cattle metagenomics data set using machine learning. Further to this, we employ a class activation map of the network when applied to specific exemplars to determine, firstly, the relevance of higher level taxonomic data, and secondly, the most relevant taxa in determining the classification, according to the classifier.

Original language	English
Title of host publication	Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Publisher	IEEE
Pages	1826-1831
Number of pages	6
ISBN (Electronic)	978-1-5386-5488-0, 978-1-5386-5487-3
ISBN (Print)	978-1-5386-5489-7
DOIs	https://doi.org/10.1109/BIBM.2018.8621543
Publication status	Published (in print/issue) - 3 Dec 2018
Event	IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018 http://orienta.ugr.es/bibm2018/

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18
Internet address	http://orienta.ugr.es/bibm2018/

Keywords

Phylogeny
Feature extraction
Bioinformatics
Microorganisms
Image coding
Genomics ,
Machine learning

Access to Document

10.1109/BIBM.2018.8621543

Integrative data analysis for the prediction of metagenomic functions
Wassan, J. T. (Author), Zheng, H. (Supervisor), Wang, H. (Supervisor) & Browne, F. (Supervisor), Mar 2020
Student thesis: Doctoral Thesis

File

Cite this

@inproceedings{14ec9148d0644fcdb4875fce5b3ef471,

title = "Phylogeny-Aware Deep 1-Dimensional Convolutional Neural Network for the Classification of Metagenomes",

abstract = "This paper evaluates a novel approach to the integration of biological domain knowledge relating to the natural evolutionary structure of microbial community data to classifying 16S rDNA sequence samples. Specifically, we evaluate the use of phylogenetic trees in addition to amplicon sequence variant abundance in samples for the classification of a processed cattle metagenomics data set using machine learning. Further to this, we employ a class activation map of the network when applied to specific exemplars to determine, firstly, the relevance of higher level taxonomic data, and secondly, the most relevant taxa in determining the classification, according to the classifier.",

keywords = "Phylogeny, Feature extraction, Bioinformatics, Microorganisms, Image coding, Genomics ,, Machine learning",

author = "Timmy Manning and Wassan, \{Jyotsna Talreja\} and Cintia Palu and Wang, \{Haiying / HY\} and Browne Fiona and Huiru Zheng and Brian Kelly and Paul Walsh",

year = "2018",

month = dec,

day = "3",

doi = "10.1109/BIBM.2018.8621543",

language = "English",

isbn = "978-1-5386-5489-7",

pages = "1826--1831",

booktitle = "Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ; Conference date: 03-12-2018 Through 06-12-2018",

url = "http://orienta.ugr.es/bibm2018/",

}

Manning, T, Wassan, JT, Palu, C, Wang, HHY , Fiona, B , Zheng, H, Kelly, B & Walsh, P 2018, Phylogeny-Aware Deep 1-Dimensional Convolutional Neural Network for the Classification of Metagenomes. in Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp. 1826-1831, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621543

Phylogeny-Aware Deep 1-Dimensional Convolutional Neural Network for the Classification of Metagenomes. / Manning, Timmy; Wassan, Jyotsna Talreja; Palu, Cintia et al.
Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. p. 1826-1831.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Phylogeny-Aware Deep 1-Dimensional Convolutional Neural Network for the Classification of Metagenomes

AU - Manning, Timmy

AU - Wassan, Jyotsna Talreja

AU - Palu, Cintia

AU - Wang, Haiying / HY

AU - Fiona, Browne

AU - Zheng, Huiru

AU - Kelly, Brian

AU - Walsh, Paul

PY - 2018/12/3

Y1 - 2018/12/3

N2 - This paper evaluates a novel approach to the integration of biological domain knowledge relating to the natural evolutionary structure of microbial community data to classifying 16S rDNA sequence samples. Specifically, we evaluate the use of phylogenetic trees in addition to amplicon sequence variant abundance in samples for the classification of a processed cattle metagenomics data set using machine learning. Further to this, we employ a class activation map of the network when applied to specific exemplars to determine, firstly, the relevance of higher level taxonomic data, and secondly, the most relevant taxa in determining the classification, according to the classifier.

AB - This paper evaluates a novel approach to the integration of biological domain knowledge relating to the natural evolutionary structure of microbial community data to classifying 16S rDNA sequence samples. Specifically, we evaluate the use of phylogenetic trees in addition to amplicon sequence variant abundance in samples for the classification of a processed cattle metagenomics data set using machine learning. Further to this, we employ a class activation map of the network when applied to specific exemplars to determine, firstly, the relevance of higher level taxonomic data, and secondly, the most relevant taxa in determining the classification, according to the classifier.

KW - Phylogeny

KW - Feature extraction

KW - Bioinformatics

KW - Microorganisms

KW - Image coding

KW - Genomics ,

KW - Machine learning

U2 - 10.1109/BIBM.2018.8621543

DO - 10.1109/BIBM.2018.8621543

M3 - Conference contribution

SN - 978-1-5386-5489-7

SP - 1826

EP - 1831

BT - Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

PB - IEEE

T2 - IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Y2 - 3 December 2018 through 6 December 2018

ER -

Phylogeny-Aware Deep 1-Dimensional Convolutional Neural Network for the Classification of Metagenomes

Abstract

Conference

Keywords

Access to Document

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Student theses

Integrative data analysis for the prediction of metagenomic functions

Cite this