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TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction

MOTIVATION: Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel ma...

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Detalles Bibliográficos
Autores principales: Sharma, Divya, Paterson, Andrew D, Xu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750934/
https://www.ncbi.nlm.nih.gov/pubmed/32449747
http://dx.doi.org/10.1093/bioinformatics/btaa542
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author Sharma, Divya
Paterson, Andrew D
Xu, Wei
author_facet Sharma, Divya
Paterson, Andrew D
Xu, Wei
author_sort Sharma, Divya
collection PubMed
description MOTIVATION: Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently, as compared to using a single CNN ignoring OTU correlations. RESULTS: We used simulated datasets containing 168 OTUs in 200 cases and 200 controls for model testing. Thirty-two OTUs, potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity. We also implemented this novel method in two human microbiome studies: (i) Cirrhosis with 118 cases, 114 controls; (ii) type 2 diabetes (T2D) with 170 cases, 174 controls; to demonstrate the model’s effectiveness. Extensive experimentation and comparison against conventional machine learning techniques yielded encouraging results. We obtained mean AUC values of 0.88, 0.92, 0.75, showing a consistent increment (5%, 3%, 7%) in simulations, Cirrhosis and T2D data, respectively, against the next best performing method, Random Forest. AVAILABILITY AND IMPLEMENTATION: https://github.com/divya031090/TaxoNN_OTU. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-77509342020-12-28 TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction Sharma, Divya Paterson, Andrew D Xu, Wei Bioinformatics Original Papers MOTIVATION: Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently, as compared to using a single CNN ignoring OTU correlations. RESULTS: We used simulated datasets containing 168 OTUs in 200 cases and 200 controls for model testing. Thirty-two OTUs, potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity. We also implemented this novel method in two human microbiome studies: (i) Cirrhosis with 118 cases, 114 controls; (ii) type 2 diabetes (T2D) with 170 cases, 174 controls; to demonstrate the model’s effectiveness. Extensive experimentation and comparison against conventional machine learning techniques yielded encouraging results. We obtained mean AUC values of 0.88, 0.92, 0.75, showing a consistent increment (5%, 3%, 7%) in simulations, Cirrhosis and T2D data, respectively, against the next best performing method, Random Forest. AVAILABILITY AND IMPLEMENTATION: https://github.com/divya031090/TaxoNN_OTU. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-05-25 /pmc/articles/PMC7750934/ /pubmed/32449747 http://dx.doi.org/10.1093/bioinformatics/btaa542 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Sharma, Divya
Paterson, Andrew D
Xu, Wei
TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction
title TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction
title_full TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction
title_fullStr TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction
title_full_unstemmed TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction
title_short TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction
title_sort taxonn: ensemble of neural networks on stratified microbiome data for disease prediction
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750934/
https://www.ncbi.nlm.nih.gov/pubmed/32449747
http://dx.doi.org/10.1093/bioinformatics/btaa542
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