PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data.
IEEE J Biomed Health Inform
; 24(10): 2993-3001, 2020 10.
Article
em En
| MEDLINE
| ID: mdl-32396115
ABSTRACT
Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introduce PopPhy-CNN, a novel convolutional neural network (CNN) learning framework that effectively exploits phylogenetic structure in microbial taxa for host phenotype prediction. Our approach takes an input format of a 2D matrix representing the phylogenetic tree populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. We show the competitiveness of our model compared to other available methods using nine metagenomic datasets of moderate size for binary classification. With synthetic and biological datasets, we show the superior and robust performance of our model for multi-class classification. Furthermore, we design a novel scheme for feature extraction from the learned CNN models and demonstrate improved performance when the extracted features. PopPhy-CNN is a practical deep learning framework for the prediction of host phenotype with the ability of facilitating the retrieval of predictive microbial taxa.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Predisposição Genética para Doença
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Metagenômica
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Microbioma Gastrointestinal
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE J Biomed Health Inform
Ano de publicação:
2020
Tipo de documento:
Article