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Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks

Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and ap...

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Autores principales: Voznica, J., Zhukova, A., Boskova, V., Saulnier, E., Lemoine, F., Moslonka-Lefebvre, M., Gascuel, O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258765/
https://www.ncbi.nlm.nih.gov/pubmed/35794110
http://dx.doi.org/10.1038/s41467-022-31511-0
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author Voznica, J.
Zhukova, A.
Boskova, V.
Saulnier, E.
Lemoine, F.
Moslonka-Lefebvre, M.
Gascuel, O.
author_facet Voznica, J.
Zhukova, A.
Boskova, V.
Saulnier, E.
Lemoine, F.
Moslonka-Lefebvre, M.
Gascuel, O.
author_sort Voznica, J.
collection PubMed
description Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep.
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spelling pubmed-92587652022-07-07 Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks Voznica, J. Zhukova, A. Boskova, V. Saulnier, E. Lemoine, F. Moslonka-Lefebvre, M. Gascuel, O. Nat Commun Article Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep. Nature Publishing Group UK 2022-07-06 /pmc/articles/PMC9258765/ /pubmed/35794110 http://dx.doi.org/10.1038/s41467-022-31511-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Voznica, J.
Zhukova, A.
Boskova, V.
Saulnier, E.
Lemoine, F.
Moslonka-Lefebvre, M.
Gascuel, O.
Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
title Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
title_full Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
title_fullStr Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
title_full_unstemmed Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
title_short Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
title_sort deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258765/
https://www.ncbi.nlm.nih.gov/pubmed/35794110
http://dx.doi.org/10.1038/s41467-022-31511-0
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