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

被引：0

作者：

Manning, Timmy ^{[1
]}

Wassan, Jyotsna Talreja ^{[2
]}

Palu, Cintia ^{[1
]}

Wang, Haiying ^{[2
]}

Browne, Fiona ^{[2
]}

Zheng, Huiru ^{[2
]}

Kelly, Brian ^{[1
]}

Walsh, Paul ^{[1
]}

机构：

[1] NSilico Life Sci Ltd, Cork, Ireland

[2] Ulster Univ, Sch Comp, Coleraine, Londonderry, North Ireland

来源：

PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | 2018年

基金：

欧盟地平线“2020”;

关键词：

amplicon sequence variants; convolutional neural network; machine learning; metagenomics; phylogeny; Grad-CAM; visualisation;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：1826 / 1831

页数：6

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